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Jialin DingView profile

Assistant Professor

• Machine Learning
• Optimization Techniques
• Data Management Systems
• +1 more

Jialin Ding is an Assistant Professor in the Department of Computer Science at Princeton University, with an appointment starting September 1, 2025. His research focuses on applying machine learning and optimization techniques to enhance data management systems. Prior to this role, he worked as an Applied Scientist at AWS and earned his Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) in 2022. His work has been honored with the Meta Research PhD Fellowship. Education: Ph.D., Computer Science, Massachusetts Institute of Technology, 2022 Research Interests: Jialin Ding explores the intersection of machine learning and systems, emphasizing practical applications in data management. His work aims to leverage optimization algorithms and AI-driven approaches to improve system efficiency and scalability. Specific areas include database systems, distributed computing, and adaptive resource allocation. Awards: Meta Research PhD Fellowship Professional Background: Before joining Princeton, he contributed to AWS as an Applied Scientist, gaining industry experience in cloud computing and large-scale system design. His academic and industrial experiences inform his research, bridging theoretical advancements with real-world system challenges.

Vladimir VinogradovView profile

Professor

• Stochastic Analysis
• Financial Mathematics
• Actuarial Mathematics
• +15 more

Vladimir Vinogradov is a Professor in the Department of Mathematics at the College of Arts and Sciences, Ohio University. He is based in Morton Hall 579 and can be reached at vinograd@ohio.edu. His academic career reflects long-standing engagement in probabilistic and statistical theory with applications in finance, actuarial science, and population dynamics. Research Interests: His work spans a wide spectrum of stochastic processes and statistical methodologies. Key areas include Stochastic Analysis, Lévy and Markov Processes, Branching and Particle Systems, Fluctuation and Extreme Value Theory, Large Deviations, and Asymptotic Expansions. He also contributes to Distribution Theory, Saddlepoint Approximations, Generalized Linear Models, and probabilistic models in Population Genetics. Grants and Awards: Dr. Vinogradov has received sustained research support, including multiple Ohio University Research Challenge Program Grants (1999–2003), NSERC Canada Individual Research Grants (1995–2003), and a Fields Institute Conference Grant (2014). He also received the BC Asia Pacific Scholars' Award and several UNBC Conference Travel Grants. Education: Ph.D., Moscow State University Professional Activities: He co-organized the International Conference on Analysis, Applications and Computations in memory of Lee Lorch in 2014. His funding history indicates active collaboration and academic leadership. There is no mention of advising students or lab affiliations in the provided text.

Oskar HallatschekView profile

Associate Professor

• Biological Physics
• Evolutionary Dynamics
• Population Genetics
• +3 more

Oskar Hallatschek is an Associate Professor and McAdams Chair in the Department of Physics and Integrative Biology at the University of California, Berkeley. His research focuses on biological physics and evolutionary dynamics, particularly how collective patterns emerge from heterogeneous microbial populations. He leads the Hallatschek Lab, which maintains collaborations with institutions like the University of Leipzig. Research Emphasis: Microbial systems, population genetics, non-equilibrium statistical physics, evolutionary adaptation, and mechanical interactions in cellular populations. Publications span journals like Nature , PNAS , and Science , with recent work on allele surfing, jamming in microbial colonies, and long-range dispersal effects. Grants: Supported by NIH and NSF, as indicated by institutional logos in the lab web presence.

Jeffrey S. ChaseView profile

Professor

• Distributed Systems
• Operating Systems
• Cloud Computing
• +7 more

Jeffrey S. Chase is a Professor of Computer Science at Duke University in the Department of Computer Science within Trinity College of Arts & Sciences. He has held this position since 2006 and currently serves as Director of Graduate Studies. His academic career spans decades with significant contributions to distributed systems research. Chase's research focuses on operating systems and distributed systems, with particular emphasis on cloud infrastructure services, trust management and security in cloud environments, energy-aware computing, and network storage. His work has evolved to center on integrity and trust: where it comes from, how we represent it, how we reason about it in software, and how we build software systems worthy of trust. His research group has developed notable systems including ExoGENI, Silver, and ADAMANT. His recent publications demonstrate his continued focus on secure data sharing architectures, networking for multi-tenant data centers, and federated authorization systems. These works collectively address fundamental challenges in distributed computing related to security, privacy, and network performance in cloud environments. Chase has received significant research funding including the CC*Data: ImPACT project (2017-2022), Collaborative Research: CICI: Secure and Resilient Architecture (2016-2020), and TWC: Frontier: Collaborative: Rethinking Security in the Era of Cloud Computing (2013-2019). He has taught advanced courses including CPS 512: Advanced Distributed Systems, CPS 310: Operating Systems, and CPS 510: Advanced Operating Systems. His teaching philosophy emphasizes deep understanding of core distributed systems concepts with practical applications to modern cloud infrastructure.

Brett Hemenway FalkView profile

Researcher

• Cryptography
• Blockchain
• Computer Security
• +5 more

Brett Hemenway Falk is a Researcher in the Department of Computer and Information Science at the University of Pennsylvania. He serves as director of the Crypto and Society Lab, focusing on privacy and security in digital environments and facilitating transparency and trust. His work combines rigorous mathematical approaches with practical implementations in cryptography and blockchain technology. Education: Sc.B. in Mathematics from Brown University Ph.D. in Mathematics from UCLA Research Interests include: Cryptography and secure multi-party computation protocols Blockchain technology (cross-chain interoperability, financial network stability, on-chain governance) Privacy-preserving algorithms and data security Coding theory applications in secure systems Scientific Contributions include: Developing practical secure computation protocols Advancing ORAM (Oblivious RAM) architectures Analyzing decentralized governance mechanisms Exploring DeFi network stability and token economics His teaching activities feature the popular MCIT 582 Blockchain course at Penn. Research funding comes from NSF, DARPA, IARPA, ONR, ARL, NIH , and the Laura and John Arnold Foundation.

Huy T TranView profile

Assistant Professor

• Autonomy
• Reinforcement Learning
• Artificial Intelligence
• +5 more

Huy T Tran is an Assistant Professor in the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign's College of Engineering, with additional appointments at the Applied Research Institute. His research focuses on the intersection of robotics, artificial intelligence, and multi-agent systems, with applications spanning autonomous navigation, critical infrastructure resilience, and intelligent transportation. Dr. Tran earned his Ph.D. in Aerospace Engineering from Georgia Institute of Technology in 2015, following advanced degrees from Georgia Tech and University of Wisconsin-Madison. His academic journey includes research assistant professor positions before achieving his current assistant professor role in 2021. He previously worked as a Senior Multi-Disciplinary Systems Engineer at The MITRE Corporation and served as a Visiting Scholar at the Air Force Institute of Technology. His research interests encompass Autonomy, Reinforcement Learning, Artificial Intelligence, Machine Learning, Robotics, Multiagent Systems, Intelligent Transportation Systems, and Critical Infrastructure Resilience. As director of the Lab for Intelligent Robots and Agents (LIRA), he leads cutting-edge research in autonomous systems that interact with humans and other robots. His work has evolved from foundational resilience modeling in aerospace systems toward increasingly sophisticated AI applications in multi-robot coordination and explainable decision-making. Dr. Tran's publication record demonstrates a clear trajectory toward explainable AI and human-AI collaboration, with recent work focusing on generating explanations for reinforcement learning policies, coordination in ad hoc teams, and neuro-symbolic approaches to robot policy interpretation. His research bridges theoretical advances with practical applications in air traffic control, field robotics, and critical infrastructure management. Best Paper Award: Theoretical (2016 Complex Adaptive Systems Conference) Selected for oral presentation at IROS 2023 Workshop 27% full paper acceptance rate at AAMAS 2022 44% acceptance rate at ICRA 2020 As an educator, Dr. Tran teaches core aerospace courses including Computational Systems Engineering, Aerospace Numerical Methods, and Reinforcement Learning. He has secured significant research funding from NASA's Transformational Tools and Technologies program, ARL A2I2 program, ONR Science of AI program, and DARPA. His current projects span ad hoc teaming in multi-robot systems, collective autonomous air mobility, hierarchical reinforcement learning, and interpretable AI agents.

Flavio EspositoView profile

Associate Professor

• Cyber-physical Systems
• Networked Systems
• Network Virtualization
• +5 more

Flavio Esposito is an Associate Professor in the Computer Science Department at Saint Louis University's School of Engineering. He also serves as a Research Institute Fellow and CS Graduate Coordinator. His office is located in ISE 234D at 3450 Lindell Blvd, St. Louis, MO. Dr. Esposito's research focuses on cyber-physical systems and networked systems, including network virtualization, network management, Software-Defined Networks (SDN), network architectures, and wireless networks. He has a strong interest in interdisciplinary applications of these technologies to medicine and agriculture. His work bridges theoretical networking concepts with practical implementations. His publications span key areas in networking research, with recent work focusing on congestion control algorithms, virtual network embedding, recursive network architectures, and edge computing applications. The research trends show a progression from foundational networking protocols toward more sophisticated applications integrating machine learning, edge computing, and cyber-physical systems, with increasing emphasis on real-world applications in diverse domains. Outstanding Graduate Mentoring Faculty Award from the School of Engineering (2021) Finalist for the Undergraduate Mentoring Award in the College of Arts and Sciences Multiple NSF research awards including US Ignite, ICE-T, CNS Core, CC* Integration, CPS:TTP, and ModernCARE projects COMCAST Innovation Fund Award (January 2020) International Center for Responsible Gaming (ICRG) Award ($150K) Dr. Esposito actively mentors PhD and MS students, with numerous current and past students who have gone on to positions at major tech companies, universities, and research institutions. He has been a Principal Investigator on multiple significant research grants totaling millions of dollars. He co-founded Spaghetti Code Labs with former PhD student Alessandro Sangiorgi, whose cybersecurity educational app WeeNet has achieved 5.7M+ downloads. He leads several research labs and teams focused on cyber-physical systems, with current openings for PhD students, visiting researchers, and postdocs working on networks, learning, edge computing, and applications to medicine and agriculture. His teams have developed numerous software systems including Software Mutant, Neighborhood Method Prototype, VINEA, ProtoRINA, and BUtorrent.

Marat I. LatypovView profile

Assistant Professor

• Materials Science
• Computational Materials Science
• Materials Informatics
• +4 more

Marat I. Latypov serves as Assistant Professor in the Department of Materials Science and Engineering at the University of Arizona's College of Engineering. He is also a member of the Applied Mathematics Graduate Interdisciplinary Program and leads the Materials Informatics Lab. His research spans computational materials science, sustainable alloy design, and machine learning applications for materials development. Dr. Latypov holds a PhD in Materials Science and Engineering from Pohang University of Science and Technology (POSTECH, South Korea, 2014) and a Dipl.-Ing. in Engineering Physics from Ufa State Aviation Technical University (Russia, 2011). His postdoctoral training included appointments at Georgia Tech/CNRS in France and the University of California, Santa Barbara. His research focuses on materials informatics , physics-informed machine learning , and sustainable structural alloys . Key methodologies include graph neural networks for polycrystal mechanics, vision transformers for microstructure representation, and adaptive experimental design for materials optimization. Recent work emphasizes circular economy applications through construction waste recycling and copper mine tailings valorization. Analysis of his publication record reveals strong emphasis on computational microstructure-property linkages (35% of recent work), machine learning for materials design (30%), and sustainable materials processing (25%), with growing integration of large language models for materials knowledge extraction. NSF CAREER Award (2025) : For damage control in recycled aluminum alloys ISTI Distinguished Faculty Scholar (2024) : At Los Alamos National Laboratory Novelis Hackathon First Prize (2021) : Computer vision application Acta Materialia Outstanding Reviewer (2018) Young Researcher Award (2017) : NanoSPD7 Conference Dr. Latypov advises PhD students including Herbold Fellow Zhuocheng Huang and leads projects funded by NSF and the Grantham Foundation. Current initiatives include chalcopyrite leaching optimization for copper mining and graph neural network development for fatigue prediction. His Materials Informatics Lab maintains collaborations with Los Alamos National Laboratory, MIT, and industry partners including Novelis. The lab operates at the intersection of metallurgy , machine learning , and high-performance computing , with capabilities spanning deep learning, Bayesian inference, and cloud-based computational infrastructure. Recent news highlights participation in CODAS-HEP summer school and publication of vision transformer work in Acta Materialia.

Moïse BlanchardView profile

Assistant Professor

• Machine Learning Theory
• Online Algorithms
• Optimization
• +4 more

Moïse Blanchard is an Assistant Professor and Tennenbaum Early Career Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, having joined in August 2025. Previously, he was a Postdoctoral Fellow at Columbia University Data Science Institute. His academic journey includes a Ph.D. in Operations Research from MIT (2024), and M.Sc. and B.Sc. degrees in Applied Mathematics from École Polytechnique. Blanchard's research focuses on the intersection of machine learning theory, statistics, and optimization. His work addresses fundamental questions in universal learning, online algorithms, and convex optimization under memory constraints. His research program explores learnability under minimal assumptions, query complexity/memory tradeoffs, and decision-making in adversarial environments. This work has significant implications for theoretical computer science, operations research, and statistical learning theory. His publications reveal a strong emphasis on foundational aspects of learning theory and optimization. Recent work demonstrates expertise in universal learning frameworks, memory-constrained optimization, and probabilistic analysis of combinatorial problems. His research often bridges theoretical computer science with practical optimization challenges, particularly in contexts where traditional i.i.d. assumptions don't hold. Columbia DSI postdoctoral fellowship, 2024 INFORMS Transportation Science & Logistics (TSL) best student paper award, 2023 Air Force Office of Scientific Research Grant (AFOSR), with Prof. Patrick Jaillet, 2023 COLT 2022 Best student paper runner-up Bronze medal, Alibaba Global Mathematics Competition, 2022 Blanchard has received significant research funding including an Air Force Office of Scientific Research Grant. His work has been recognized with multiple prestigious awards, including the INFORMS TSL best student paper award for his research on the k-Traveling Salesman Problem. His extensive publication record in top venues demonstrates a strong research trajectory with impactful contributions to theoretical machine learning and optimization.

Keenan AlbeeView profile

Assistant Professor

• Robotic Motion Planning
• Autonomous Systems
• Optimal Control
• +5 more

Keenan Albee is a Robotics Technologist at NASA’s Jet Propulsion Laboratory and an incoming Assistant Professor at the University of Southern California (starting Fall 2025). His research focuses on autonomous robotics for extreme environments including lunar missions, microgravity, and underwater operations. Education: Ph.D. in Aeronautics and Astronautics (Autonomous Systems), MIT (2022) S.M. in Aeronautics and Astronautics, MIT (2019) B.S. in Mechanical Engineering, Columbia University (2017) Albee’s work integrates optimal control , reinforcement learning , and motion planning to develop autonomy for mobile robotic systems operating under uncertainty. His expertise spans space robotics , microgravity systems , and underwater robotics , with a focus on environment-aware algorithm design. Recent research includes parametric information-aware motion planning (RATTLE algorithm), distributed multi-agent exploration, and robust control for uncooperative targets. His publications highlight on-orbit validation of autonomy algorithms via NASA’s Astrobee platform and upcoming lunar missions. Scientific Awards: NASA Space Technology Research Fellowship (2022) Albee actively develops open-source autonomy frameworks and will establish the Laboratory for Autonomous Systems in Exploration and Robotics (LASER) at USC. His work bridges theoretical control methods with real-world deployment, including first-of-its-kind achievements in space robotics.

Shimon Y. NofView profile

Professor

• Collaborative Robotics
• Cyber-Physical Systems
• Industrial Automation
• +10 more

Shimon Y. Nof is a Professor of Industrial Engineering at Purdue University's PRISM Center , where he directs NSF-industry supported research on collaborative robotics, cyber-physical systems, and industrial automation. He has held visiting positions at MIT and universities across six countries. Education: B.Sc./M.Sc. in Industrial Engineering & Management (Technion, Israel), Ph.D. in Industrial & Operations Engineering (University of Michigan) Key Achievements: Pioneered computer-aided facility design and Collaborative Control Theory (CCT), with applications spanning factories of the future, agricultural robotics, and transportation security systems His research focuses on cyber-supported integration of distributed e-Work systems and robotics, including precision agriculture with sensor networks. The PRISM Center under his leadership has developed groundbreaking protocols like Best Matching Protocol (BMP) and HUB-CI telerobotics, with real-world implementations across 400+ labs globally. Scientific honors include: Engelberger Medal (2002) Induction into Purdue's Book of Great Teachers (1999) Leadership roles in IFPR and IFAC Multiple book awards from Association of American Publishers The PRISM Global Research Network (est. 2001) now extends his work through 18 books and 6 patents, including the FTTP-TIF communication protocol and Facility Sensor Network (FSN) technology currently applied in greenhouse robotic operations.

Amir BahadoriView profile

Professor

• Radiation Protection
• Radiation Transport
• Radiation Dosimetry
• +5 more

Amir Bahadori serves as Professor and Nuclear Engineering Program Director in the Department of Mechanical and Nuclear Engineering at Kansas State University's Carl R. Ice College of Engineering, holding the Hal and Mary Siegele Professorship in Engineering. He directs the Radiological Engineering Analysis Laboratory (REAL) and established the Institute for Radiation Health Studies (IRHS) in 2024, focusing on radiation protection, space radiation environments, and radiation health effects. His educational background includes: Ph.D. in Biomedical Engineering, University of Florida (2012) M.S. in Nuclear Engineering Sciences, University of Florida (2010) B.S. in Mechanical Engineering and Mathematics, Kansas State University (2008) Bahadori's research spans radiation transport modeling, dosimetry, and risk assessment with applications in space exploration, medical physics, and radiation epidemiology. He develops computational frameworks for radiation exposure scenarios and biological response prediction, emphasizing space radiation protection for Artemis missions and chronic exposure studies through the Million Person Study collaboration. Analysis of his recent publications reveals dominant themes in space radiation measurement (Artemis missions), radiation epidemiology (Million Person Study innovations), and advanced detection systems (miniaturized neutron spectrometers). His work increasingly integrates big data approaches for radiation risk assessment and electrostatic shielding concepts for deep-space exploration. His scientific recognition includes: NASA Graduate Student Research Fellowship (2009) Certified Health Physicist designation Big 12 faculty fellowship (2022-2023) NCRP council election (2024) Two USPTO patents Bahadori secures substantial research funding from NASA for space radiation instrumentation, Department of Energy projects via the Kansas City National Security Campus, and collaborative epidemiological studies. He mentors nuclear engineering graduate students while leading interdisciplinary teams developing radiation protection solutions for aerospace and medical applications. His laboratory infrastructure includes the REAL with Beocat high-performance computing resources, radiation detectors, and a 3D printer, plus the IRHS with a Precision X-ray XRad320 irradiator and radon chamber. These facilities support collaborations across K-State colleges and external organizations for radiation health effect studies.

Charity NyeleleView profile

Assistant Professor

• Environmental Justice
• Ecosystem Services
• Urban Forestry
• +5 more

Charity Nyelele is an Assistant Professor in the Environmental Sciences department at the University of Virginia. Her research bridges human well-being and environmental systems, focusing on biodiversity, climate change, and ecosystem services through the lens of environmental justice and equity. Specializes in urban forestry and socio-ecological synthesis Active in climate justice, carbon sequestration, and stormwater management Nyelele's recent work integrates machine learning and social media data to map recreational ecosystem services and optimize tree planting frameworks. She has developed multi-objective decision support tools to address urban ecosystem service trade-offs and leads research in fire-driven ecosystem restoration across Western US forests. She teaches courses on Environmental and Climate Justice , Management of Forest Ecosystems , and co-instructs Politics, Science, and Values . Contact: hbt3mb@virginia.edu

Luis Duffaut EspinosaView profile

Assistant Professor

• Control Theory
• Estimation Theory
• Robotics
• +7 more

Summary Luis A. Duffaut Espinosa is an Assistant Professor in the Department of Electrical and Biomedical Engineering at the University of Vermont (UVM), affiliated with the College of Engineering and Mathematical Sciences. His research focuses on control theory, estimation, robotics, and nonlinear systems with applications in autonomy, quantum control, and environmental monitoring. He holds a Ph.D. in Electrical and Computer Engineering from Old Dominion University (2009) and has held academic positions at George Mason University and postdoctoral roles at Johns Hopkins University and the University of New South Wales. Education: Ph.D. in Electrical and Computer Engineering (2009), Old Dominion University M.S. in Mathematics (2005), Pontificia Universidad Católica del Perú B.S. in Physics (2003), Universidad Nacional de Ingeniería, Peru Research Interests: His work emphasizes data-driven control and estimation methodologies, including model-free approaches for power systems, environmental monitoring, and quantum control. Current projects include real-time data assimilation in harsh environments, resilient robotics in GPS-denied conditions, and SAR with small satellites. He co-directs the Autonomous and Intelligent Systems Research Laboratory (AIRLab) and is part of the CREATE center. Recognition: 2024 NSF CAREER Award for work on safety-aware data-driven control frameworks Teaching & Advising: He teaches courses in estimation theory, control systems, and signal processing. Advises a team of graduate and undergraduate students focusing on autonomy, robotics, and control systems. Notable students include Danial Waleed (Ph.D. 2024), Jacob Friz-Trillo (M.S. 2025), and Farnaz Boudaghi (Ph.D. candidate). Labs & Collaborations: AIRLab: Focuses on data-driven control for autonomy in robotics and engineered systems CREATE: Research on resilient energy and autonomous technologies

Marco SerafiniView profile

Assistant Professor

• Database Systems
• Distributed Systems
• Data Science
• +3 more

Marco Serafini is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences (CICS). He leads the DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and is part of the Center for Data Science. Prior to UMass, Serafini worked as a Senior Scientist at the Qatar Computing Research Institute (QCRI) and held a postdoctoral fellowship at Yahoo! Research in Barcelona. He earned his PhD in Computer Science from TU Darmstadt (Germany), where his thesis was recognized through nominations for best thesis awards across German, Swiss, and Austrian computer science societies. His research focuses on the intersection of database systems, distributed systems, and data science, emphasizing scalable architectures for big data analytics and machine learning. Key areas include computation pushdown in cloud DBMSs, graph neural network training systems, and efficient graph pattern matching. His work addresses challenges in tail latency mitigation, resource optimization, and transparent scaling of ML models. Serafini has contributed to influential systems like Arabesque (for distributed graph mining), E-Store (elastic partitioning), and Aion (event-time stream processing). He has been awarded an NSF CNS Core grant to advance scalable GNN training. His publications span top venues such as ACM SIGOPS, VLDB, and ICDE, reflecting his expertise in both theoretical foundations and practical system implementations. Professional recognition includes thesis nominations from major computer science societies and sustained contributions to open-source projects in distributed computing. Serafini advises students through the DREAM Lab, focusing on preparing the next generation of data systems researchers.