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Peter KramerView profile

Professor

• Probability Theory
• Differential Equations
• Complex Systems Modeling
• +3 more

Dr. Peter Kramer is a Professor and Department Head in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute. He holds a Ph.D. from Princeton University (1997) and has been at Rensselaer since 2000. His research focuses on applying probability theory, differential equations, and stochastic modeling to study complex systems in biology, environmental science, and neuroscience. Key areas include molecular motor transport, neuronal network analysis, and active matter dynamics. He collaborates with researchers at institutions like Arizona State University and the University of Colorado. Dr. Kramer’s work emphasizes statistical approaches to model unresolved variables in computationally intensive systems. Notable projects address cargo transport in cells, environmental stochastic modeling, and neuronal network topology inference from firing data. He organizes the Mathematical Problems in Industry Workshop and mentors students in modeling competitions like the Mathematical Contest in Modeling. His recent publications (2017–2022) explore topics such as stochastic field theories for active matter, molecular motor cooperation, and Bayesian inference in dynamical systems. His research bridges applied mathematics with interdisciplinary applications, emphasizing both theoretical rigor and practical computational methods.

Charalampos ChelmisView profile

Associate Professor

• Network Science
• Big Data Analytics
• Machine Learning
• +4 more

Charalampos Chelmis is an Associate Professor in the Department of Computer Science at the University at Albany, State University of New York (SUNY). He directs the Intelligent Big Data Analytics, Applications, and Systems (IDIAS) Lab , focusing on cutting-edge research in data-intensive computing for social good. Previously, he was a Senior Research Associate at the University of Southern California. Education: PhD in Computer Science, University of Southern California (2013) MS in Computer Science, University of Southern California (2010) BEng in Computer Engineering and Informatics, University of Patras, Greece (2007) Research Interests: Dr. Chelmis specializes in Network Science, Big Data analytics, and scalable algorithms for complex, high-dimensional datasets. His work integrates graph theory, machine learning, and data mining to address real-world challenges like energy efficiency and social network analysis. Key applications include cyberbullying detection, demand response in smart grids, and computational social science. Publication Trends: His recent articles (2015-2016) demonstrate a strong focus on scalable algorithms for social networks and energy systems, including FPGA optimizations, dynamic graph computation, and machine learning for time-series prediction. Dominant themes include social influence modeling, big data clustering, and hardware-accelerated graph processing. Awards & Honors: DARPA Forecasting Chikungunya Challenge Methodology Prize (2015) SUNY-B Faculty Research Award (2018) UAlbany's Next Research Frontier Award (2019) Fulbright Mutual Educational Exchange Grant (2008) Grants & Advising: He leads an NSF-funded project ( CRII: III: Adding Exploratory Statistical Analysis and Prediction Support to SPARQL ) to simplify semantic data analysis. As director of IDIAS Lab, he mentors PhD and Master's students in projects like noisy-label learning and cyberbullying detection. Labs & Teams: The IDIAS Lab develops solutions for socially important problems, collaborating with industry partners (e.g., Google, Oracle) and focusing on scalable ML systems and semantic data integration.

• Clinical Trials Design
• Biostatistics
• Health Interventions
• +6 more

Dr. Andres Azuero holds dual faculty appointments at The University of Alabama at Birmingham (UAB) as Professor in the School of Nursing's Nursing Family, Community & Health Systems department and the School of Public Health's Biostatistics department. He also serves in multiple senior research roles across UAB's School of Medicine, College of Arts and Sciences, and specialized centers including the Lung Health Center and Minority Health & Research Center. His academic career spans over 15 years, with prior roles including Associate Professor and Program Director in nursing research. Educational background includes a Ph.D. in Biostatistics (UAB, 2008), an MBA from Louisiana State University (2000), and a BS in Industrial Engineering from a foreign university (1997). He is fluent in Spanish for professional collaboration. Research focuses on statistical and machine learning methodologies applied to clinical trials, HIV neurocognitive disorders, cancer care management, and telehealth innovations. Key projects include randomized controlled trials on cognitive training for HIV patients, remote symptom monitoring systems for oncology populations, and interventions addressing healthcare disparities in the Deep South region. Collaborative work spans multidisciplinary teams across nursing, public health, medicine, and engineering. His recent studies emphasize digital phenotyping using smartphone data, opioid overdose intervention efficacy, and family caregiver decision-support systems in advanced cancer care. Professional service includes directing research proposal design initiatives and serving as a senior scientist in multiple translational research centers. His work bridges statistical rigor with clinical impact, particularly in marginalized populations.

Antoni Martínez BallestéView profile

Associate Professor

• Internet of Things
• Multimedia Data Networks
• Security and Privacy
• +6 more

Antoni Martínez Ballesté is an Associate Professor at the Department of Computer Engineering and Mathematics at Universitat Rovira i Virgili (URV) since 2003. He holds a PhD in Telematics Engineering from Universitat Politècnica de Catalunya (2004) and a postgraduate specialization in the European Higher Education Area (2006). He is a Senior Member of IEEE and leads the Smart Technologies research group (formerly Smart Health URV) since 2014. His research focuses on applying Information and Communication Technologies (ICT) to healthcare and quality of life improvement, with expertise in IoT, cybersecurity, and privacy-preserving systems. He has served as a visiting professor at Shanghai Maritime University (China) and LAAS-CNRS (France) and has contributed to projects like SIMPATIC and ACTUA. Education: PhD in Telematics Engineering (UPC 2004), Postgraduate Specialist in European Higher Education (URV 2006). Research interests include smart health, context-aware systems, and ethical implications of technology. He collaborates with institutions like the Crises group and the Smart Health initiative. Key contributions include privacy-aware video surveillance platforms, smart classroom infrastructure using IoT/AI, and systematic reviews in railway security and cognitive cities. His work spans 119 publications across journals, books, and conferences, with notable contributions in IEEE Access, Sensors, and Expert Systems with Applications. He advises PhD students on topics like trajectory analysis and statistical disclosure control. Teaching roles include courses on programming, data networks, and research methodology at undergraduate and master's levels. He actively organizes workshops on scientific writing and cybersecurity for PhD students, emphasizing interdisciplinary innovation in computer science and healthcare.

Andy AdlerView profile

Professor

• Medical Image Processing
• Electrical Impedance Tomography (EIT)
• Biometrics
• +3 more

Andy Adler is a Professor and Canada Research Chair (Tier II) in Biomedical Engineering at Carleton University's Faculty of Engineering and Design, Department of Systems and Computer Engineering. He specializes in Medical Image Processing, Electrical Impedance Tomography (EIT), and Biometrics. His research focuses on improving lung function monitoring, physiological signal analysis, and biometric system design. Adler teaches advanced courses like SYSC 4203 (Biomedical Instrumentation) and BIOM 5202 (Medical Image Processing), emphasizing hands-on laboratory work and project-based learning. His scientific contributions include over 80 peer-reviewed articles, with recent work on EIT applications in critical care, equine medicine, and pulmonary hypertension. Notable achievements include developing novel EIT techniques for lung ventilation/perfusion analysis and advancing biometric encryption standards. He advises graduate students in EIT system design and biomedical instrumentation, and collaborates with industry on medical device innovation. His lab, equipped with advanced imaging and signal acquisition systems, supports interdisciplinary research in bioinstrumentation and medical physics.

Qinglei CaoView profile

Assistant Professor

• High-performance Computing
• Artificial Intelligence
• Parallel Computing
• +2 more

Qinglei Cao is an Assistant Professor in Computer Science at Saint Louis University's School of Science and Engineering, where he leads the Omni Computing Lab. He holds a PhD from University of Tennessee, Knoxville and has industry experience at Cerebras Systems. His research bridges High-Performance Computing and Artificial Intelligence, focusing on parallel algorithms for scientific applications. Key areas include task-based runtime systems, sparse matrix computations, and optimization techniques for large-scale environmental modeling. Recent publications demonstrate innovations in geospatial computation precision, extreme-scale statistical modeling, and energy-efficient algorithms. His work frequently targets climate prediction and computational mathematics challenges. Awards: ACM Gordon Bell Prize for Climate Modelling (2024) Best Paper Award, IEEE CLUSTER (2020) Gordon Bell Prize Finalist (2022, 2024) He advises PhD students in HPC/AI convergence and leads research on GPU/CPU optimization for machine learning workflows.

Ken RyanView profile

Professor

• Semi-Supervised Learning
• Design of Experiments
• Statistical Computing
• +2 more

Ken Ryan is Professor of Statistics at West Virginia University and Fellow of the American Statistical Association. He serves as Associate Director overseeing statistics within the School of Mathematical and Data Sciences, coordinating research initiatives and academic programs. Ryan's research develops statistical methodologies across experimental design, semi-supervised learning, and reliability modeling. Key contributions include covariate shift solutions in regression, combinatorial algorithms for optimal designs, and Bayesian degradation models for reliability testing. His work frequently addresses high-dimensional and complex-system applications through geometric and computational approaches. His publication record demonstrates sustained innovation across statistical computing, experimental optimization, and machine learning interfaces. Recent methodological extensions address overlapping count distributions and sequential Bayesian assurance testing while maintaining foundational research on orthogonal array classification and semi-supervised regularization.

FEDERICA PASCUCCIView profile

Associate Professor

• Cyber-Physical Systems
• Artificial Intelligence
• Network Security
• +3 more

Federica Pascucci is an Associate Professor at the Department of Civil, Computer and Aeronautical Engineering, University of Rome 3. Her research spans cyber-physical systems, artificial intelligence, and sustainability in supply chains. She serves as the Rector's Delegate for coordinating the Scientific Council of the University Library System. Phone: +39 0657333227 Email: federica.pascucci@uniroma3.it Address: Via Vito Volterra 62 Her research focuses on securing industrial cyber-physical systems, strategic control algorithms, and AI applications in societal contexts. Recent work explores female entrepreneurship, blockchain in urban planning, and digital transformation in SMEs. Key trends in her 2025 publications include cyber-physical security, AI ethics, and human-centric safety systems. 2024 works emphasize sustainability paradoxes in global supply chains, particularly coffee sector dynamics.

Antonio Rubio SoláView profile

Professor

• Integrated Circuit Design
• Semiconductor Technology
• Neuromorphic Engineering
• +4 more

Antonio Rubio Solá is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the High Performance Integrated Circuits and Systems Design (HIPICS) group. He holds an M.S. in Industrial Engineering (1977) and a Ph.D. in Electronic Engineering (1982), both from UPC. His research focuses on semiconductor technology evolution, integrated circuit design, and memristor-based neuromorphic systems. Key interests include nanoelectronics, energy-efficient computing, and biomimetic circuits. Rubio has contributed to advancements in memristive logic, graphene nanoribbon devices, and fault-tolerant circuit architectures. His work bridges theoretical research and practical implementation, with notable contributions in neuromorphic hardware, in-memory computing, and radiation-hardened electronics. Recent publications emphasize memristor applications in biological systems emulation, stochastic resonance phenomena, and energy-efficient data processing. Rubio actively participates in Spain’s neuromorphic technology initiatives and promotes sustainable microelectronics education through digital tools. Publications are accessible via UPC FenixDoc and the HIPICS e-prints repository. His research is driven by interdisciplinary collaboration, addressing challenges in next-generation computing paradigms and emerging technologies.

Ethan L. MillerView profile

Professor

• Storage systems
• Security
• Archival storage
• +5 more

Ethan L. Miller is a Professor Emeritus at the University of California Santa Cruz (UCSC), affiliated with the Computer Science and Engineering Department within the Baskin School of Engineering. He previously held the Veritas Presidential Chair in Storage Systems and served as Director Emeritus of the NSF I/UCRC Center for Research in Storage Systems. His research focuses on storage systems, security, archival storage, and distributed systems. Miller received his Sc.B. from Brown University and his M.S. and Ph.D. from UC Berkeley. He has extensive industry collaboration, notably with Pure Storage, contributing over 190 patents. Key roles include co-founding the Storage Systems Research Center (SSRC) at UCSC in 2001 and directing the NSF I/UCRC Center from 2012-2019. He retired in July 2023 but continues research collaborations. Awards include IEEE Fellow (2015), ACM Distinguished Scientist (2013), and the William Gaston Prize for Academic Excellence (1987). Research highlights include work on the Ceph distributed file system, secure archival systems (POTSHARDS), and Twizzler—a data-centric OS for non-volatile memory. Over 100 peer-reviewed publications span storage architecture, security, and performance optimization. Miller also advises on intellectual property cases as an expert witness. Funding includes NSF grants (e.g., $1.2M for secure long-term storage) and industrial partnerships with companies like EMC, IBM, and Western Digital. His work emphasizes practical solutions for exascale storage challenges and data longevity.

• High Performance Computing
• Cloud Computing
• Computing Sustainability
• +3 more

Roy Rohan Basu is an Assistant Professor at the Kahlert School of Computing at the University of Utah, joining in July 2025. His research focuses on High Performance Computing, Cloud Computing, and Computing Sustainability, with an emphasis on environmental impact mitigation and resource optimization in large-scale systems. Research interests include sustainable cloud architectures, serverless computing efficiency, and reducing carbon/water footprints in computing. He explores innovative solutions for cold-start mitigation, I/O bottlenecks, and energy-efficient resource allocation. Recent work highlights include analyzing forever chemicals' impact on computing infrastructure, optimizing carbon-aware serverless scheduling, and enhancing GPU-accelerated AI workloads. His research bridges theoretical models and practical implementations, addressing real-world challenges in scalable and sustainable computing. Articles from 2022–2025 reflect a growing focus on environmental sustainability in computing systems, with contributions to cloud resource management, quantum computing evaluation, and manufacturing process digital twins. No scientific awards or grants are explicitly listed in the provided information. His current role emphasizes advancing sustainable computing through interdisciplinary approaches.

Jon LongtinView profile

Professor

• Thermal Engineering
• Laser Material Processing
• Heat Transfer
• +3 more

Jon Longtin is a Professor and Interim Chair in the Department of Mechanical Engineering at Stony Brook University. He has been with the university since 1996, contributing to academic leadership and cutting-edge research in thermal engineering and laser material processing. Education: Ph.D., Mechanical Engineering, University of California, Berkeley (1995) M.S., Mechanical Engineering, University of Cincinnati (1991) B.S., Mechanical Engineering, University of Cincinnati (1989) Research Interests: Thermophysical behavior measurement using laser light for liquids and solids Ultra-high-intensity laser interactions with materials for manufacturing Thermoelectric device fabrication via thermal spray and laser processing Energy-efficient thermal systems and decarbonization strategies Articles Trends: Recent work focuses on smart energy systems (e.g., automated heating stoves), free-space optical networks, and pandemic-related ventilator design. Earlier contributions include topology optimization, thermosyphon applications, and scramjet sensor development. Awards: 1996 JSPS Postdoctoral Fellowship 1997 NSF CAREER Award 1997 PECASE Award 1998 Excellence in Teaching Award Grants & Labs: Funding sources: NSF, NASA, Department of Defense (ESTCP) Research teams collaborate on energy storage, laser additive manufacturing, and thermal spray technologies.

Bo ZhaoView profile

Assistant Professor

• Distributed Systems
• Machine Learning Systems
• Large-Scale Data Processing
• +2 more

Bo Zhao is an Assistant Professor in the Department of Computer Science at Aalto University, leading the Aalto Data-Intensive System group (ADIS). His research focuses on building efficient data-intensive systems across multiple layers, from scalable machine learning systems to distributed data management systems. Research Focus: Scalable machine learning systems Distributed data processing Hardware-software co-design Quantum computing systems High-performance computing Current Projects: AthenaRL (scalable RL systems), LARA (quantum ML algorithms), FlexMoE (efficient mixture-of-experts systems). His group develops systems enabling ML deployment across hardware from quantum computers to mobile devices. Education & Career: PhD from Humboldt-Universität zu Berlin. Previously at Queen Mary University of London, Imperial College London, and Amazon Web Services. Research published in SOSP, VLDB, USENIX ATC.

• Mathematical Statistics
• Non-Parametric Statistics
• Bayesian Non-Parametrics
• +5 more

Paulo Serra is an Assistant Professor at the Mathematics Department of the Faculty of Science at Vrije Universiteit Amsterdam. He holds additional roles including Board Member of the MSc Internship Board for Business Analytics, Examination Board Mathematics and Business Analytics, and the Mathematical Statistics section of VVSOR. Previously, he served as Assistant Professor at Eindhoven University of Technology and held postdoctoral positions at the University of Amsterdam, University of Goettingen, and UNINOVA in Portugal. Education: PhD in Mathematical Statistics (Eindhoven University of Technology, 2009–2013) MSc in Mathematical Sciences (Utrecht University, 2006–2008) Licenciatura in Applied Mathematics (New University of Lisbon, 2000–2005) Research Interests: Focus on non-parametric mathematical statistics, including Bayesian non-parametrics, spline estimators, statistical tracking of time-varying parameters, quantile regression, and Markov processes. His work bridges theoretical foundations with practical applications in algorithm design and implementation, particularly in biomedical and engineering domains. Teaching: Teaches courses in Stochastics, Statistics for Business Analytics, and Stochastic Processes for Finance at VU Amsterdam, as well as the Mastermath Bayesian Statistics course. Provides introductory materials on Probability and Statistics using Python and Jupyter Notebook. Supervision: Currently co-supervises three PhD students, one MSc student, two BSc students, and one MSc internship. Offers thesis projects in areas aligned with his research expertise. Key Themes in Publications: Recent work emphasizes medical applications (e.g., perioperative patient deterioration prediction) and methodological advancements in robust estimation, nonparametric Bayesian techniques, and adaptive algorithms. Earlier contributions include fuzzy logic systems for spacecraft thermal monitoring and network dimension estimation in inhomogeneous graphs.

Susmit ShannigrahiView profile

Assistant Professor

• Network Protocols
• Future Internet Architectures
• 5G Mobile Networks
• +8 more

Susmit Shannigrahi is an Assistant Professor in the Department of Computer Science at Tennessee Tech University. His work focuses on advanced networking protocols, future internet architectures, and big data management. He leads research in areas like 5G networks, IoT security, and decentralized data replication algorithms. His research interests include optimizing network protocols for data-intensive applications, securing vehicular and smart grid systems, and developing frameworks like CLEDGE and Hydra for edge computing and distributed storage. He has contributed to projects such as NDN-based data distribution for genomics and climate research, and has pioneered solutions like AquaCam for environmental monitoring. Shannigrahi's recent work emphasizes hybrid networking approaches (e.g., NDN-IP integration), proactive routing strategies using AI, and scalable decentralized systems. His projects often involve collaboration with domain scientists to address real-world challenges in big data transfer and storage. He holds a strong record of innovation in testbed deployments, protocol design, and cross-disciplinary applications of networking principles. His lab focuses on practical implementations of theoretical networking concepts in fields like smart grids, autonomous vehicles, and genomics.