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Ben ShieldsView profile

Senior Lecturer

• Digital Transformation
• Sports Industry
• Media Industry
• +7 more

Ben Shields is a Senior Lecturer in Managerial Communication at the MIT Sloan School of Management, where he focuses on digital transformation in the sports, media, and entertainment industries. He teaches graduate courses including Creative Industries: Media, Entertainment, and the Arts; Social Media Management; Sports Strategy and Analytics; and Communication for Leaders. His research interests span multiple domains of digital engagement: Digital transformation strategies for sports organizations Social media management and persuasion in networked culture Data-driven team performance and storytelling Leadership communication in digital contexts Monetization strategies for digital content Fan engagement in crowded marketplaces Ben Shields' publications demonstrate a consistent focus on the intersection of data, storytelling, and performance across sports and entertainment. His work shows how organizations can leverage digital tools while maintaining authentic human connections with audiences. Recent publications emphasize the growing importance of AI in content creation and audience engagement, particularly in how data-driven teams can use narrative techniques to achieve sustained performance. His scientific achievements include: MIT's Teaching with Digital Technology Award (2020) Jamieson Prize for Excellence in Teaching (2022), MIT Sloan's most prestigious teaching award As an educator and practitioner, Shields has developed and teaches multiple executive education programs including the Global Executive Academy (where he serves as Faculty Director), Data-Driven Teams: The Art and Science of Winning, Communication and Persuasion in the Digital Age, and Maximizing Your Personal Productivity. Prior to MIT, he served as Director of Social Media and Marketing at ESPN, bringing practical industry experience to his academic work.

Amlan ChatterjeeView profile

Associate Professor

• High-Performance Computing
• Big Data
• Graph Compression
• +5 more

Dr. Amlan Chatterjee is an Associate Professor in the Department of Computer Science at California State University, Dominguez Hills. His work focuses on high-performance computing, big data analytics, and GPU-based graph compression techniques. He has held academic roles since 2015, including Assistant Professor (2015-2021) and currently serves as Associate Professor. His research explores efficient computation on large datasets using multi-core architectures and GPUs, alongside cloud computing optimization and IoT applications in aviation and health monitoring. Education: Ph.D., Computer Science, University of Oklahoma, 2014 M.S., Computer Science, State University of New York, 2009 B.Tech., Computer Science & Engineering, West Bengal University of Technology, 2007 Research Interests: Dr. Chatterjee's research spans graph compression, social network analysis, cloud resource optimization, and IoT-driven solutions for aviation safety and health monitoring. He has mentored students in projects like GPU-based big data processing and cloud computing efficiency. Awards: Graduate Computer Science Scholarship (University of Oklahoma, 2012-13) Computer Science Advisory Board Scholarship (2012) Phillips Petroleum Scholarship (2010-11) Top Undergraduate Rank (1st/68 students) Academic Contributions: He has served on numerous committees, including the Research Chair for Untenured Faculty (2016-17), IEEE conference session chairs, and accreditation boards. His teaching includes courses on data structures, operating systems, and introductory computer science.

Nesime TatbulView profile

Researcher

• Large-scale Data Management Systems
• Time Series Analytics
• Observability Data Management
• +4 more

Nesime Tatbul is a Senior Research Scientist at Intel Labs and MIT's Computer Science and Artificial Intelligence Lab (CSAIL). She leads Intel's Data Systems and AI Lab (DSAIL) and previously held a faculty position at ETH Zurich. She holds a PhD and MS from Brown University and BS/MS from Middle East Technical University (METU). Her research focuses on large-scale data management systems, learned systems, time series analytics, and observability. Key contributions include the Aurora/Borealis and S-Store systems. She has served on program committees for SIGMOD, VLDB, and CIDR, and holds roles as an ACM Distinguished Member and IEEE Senior Member. Her work spans over 70 publications, including influential contributions to stream processing and query optimization. Awards include the PVLDB Distinguished Editor Award (2023), CIDR Test of Time (2025), and ACM SIGMOD Best Paper (2021). Current projects include DSAIL, Exathlon, and Mach, advancing observability and AI-driven data systems. She also contributes to editorial roles at VLDB and PVLDB.

• Complex Event Processing
• Healthcare Informatics
• Semantic Computing
• +5 more

Fabio Persia is a Professor at the University of Naples Federico II with an extensive publication record spanning 15 years (2009-2025), demonstrating continuous academic engagement. His research portfolio spans multiple institutions through collaborations with over 70 co-authors, most notably Daniela D'Auria (43 publications), Mouzhi Ge (17), and Giovanni Pilato (14). Dr. Persia's research interests focus on the intersection of semantic computing, event processing, and practical applications. His work evolved from foundational contributions to multimedia recommender systems (2013) to developing the ISEQL interval-based surveillance event query language (2016), and most recently to healthcare AI applications. He has pioneered complex event processing frameworks for video surveillance, created multi-agent systems for epilepsy detection (PredictMed-epilepsy), and explored social sensing for personalized routing during the pandemic. His recent work increasingly integrates large language models with healthcare monitoring systems, reflecting current AI trends. Analysis of his publication trends since 2020 reveals a strong healthcare focus (65% of recent work), particularly in patient monitoring architectures, clinical decision support, and medical AI integration. His publications demonstrate consistent methodological rigor across domains, often combining semantic computing with real-time event processing to address practical challenges in healthcare and social computing contexts. Dr. Persia has served as guest editor for six special issues in the International Journal of Semantic Computing (2023-2025) covering Robotic Computing, Transdisciplinary AI, and Multimedia Computing, confirming his leadership in these research communities. His editorial roles complement his extensive publication record, which includes 25 journal articles and 76 conference papers. His collaborative research includes significant projects with Stefania Costantini on patient monitoring systems, with Mouzhi Ge on multimedia recommenders, and with Sven Helmer on interval joins and event detection. While specific grant information isn't detailed in publications, his sustained output suggests successful funding from multiple sources. His current research trajectory indicates growing emphasis on LLM integration in medical contexts and context-aware systems for public health applications.

• Distributed Computing
• Theoretical Computer Science
• Parallel Algorithms
• +4 more

Hsin-Hao Su is an Assistant Professor in the Department of Computer Science at Boston College. His research focuses on distributed computing through the lens of theoretical computer science, emphasizing parallelism, locality, communication, combinatorial optimization, symmetry breaking problems, and gossip-based algorithms. He completed his Ph.D. at the University of Michigan (2010–2015), advised by Seth Pettie, followed by a postdoctoral fellowship in Nancy Lynch's group at MIT (2015–2017). **Education:** Ph.D. in Computer Science, University of Michigan (2010–2015) Postdoctoral Associate, MIT (2015–2017) His work addresses foundational challenges in distributed systems, including efficient algorithms for shortest paths, graph decomposition, and task allocation. Notable contributions include advancing parallel and distributed approaches to clustering, matching, and quantile computations. Su has served on program committees for major conferences like ISAAC, ESA, and DISC, and held roles such as Workshop Chair (PODC 2021) and Student Travel Awards Organizer (PODC 2018). **Research Trends:** His articles emphasize algorithm design under distributed and parallel constraints, with recent focus on optimizing communication, reducing computational rounds, and bridging theoretical guarantees with practical efficiency. Topics span graph algorithms, approximation techniques, and bio-inspired methods.

Paul McDonaldView profile

Professor

• Critical Media Industries Research
• Film and Television Industries
• Digital Distribution of Entertainment Media
• +3 more

Paul McDonald is a Professor of Media Industries at King’s College London, affiliated with the Faculty of Arts and Humanities and the Department of Culture, Media and Creative Industries. He previously held roles such as Vice Dean (People and Planning) in the Faculty of Arts and Humanities and Head of the Department of Culture, Media and Creative Industries. His academic journey includes positions at the University of Nottingham, University of Portsmouth, and others. McDonald holds a PhD in Film Studies from the University of Warwick and a BA from the University of Reading. His research focuses on critical media industries studies, including film/TV industries, digital distribution, media piracy, and media’s role in urban contexts. He has authored/co-authored numerous books, such as *The Routledge Companion to Media Industries* (2021) and edited volumes on global film policies and spatial dynamics of media industries. Key projects include the *Channel 4 and British Film Culture* grant-funded study and collaborations on online distribution with Warner Bros. McDonald has received awards like the Leverhulme Research Fellowship and AHRC grants. He founded the biennial Media Industries conferences and co-edits the *International Screen Industries* book series. His work bridges academia and industry, informed by early career roles in acting, film production, and media. Teaching emphasizes critical evaluation of media industries, while public engagement covers Hollywood, piracy, and digital distribution. Current projects include six upcoming books on global media policies and platforms.

• Computational Neuroscience
• Machine Learning
• Neural Dynamics
• +5 more

Dr. Emili Balaguer-Ballester is an Associate Professor in Computational Neuroscience at Bournemouth University, UK. He co-champions the Interdisciplinary Neuroscience Research Centre and serves as Senior Fellow of the Higher Education Academy. His research spans computational neuroscience, machine learning, and virtual reality applications, with collaborations across Europe (University of Barcelona, Heidelberg University, IDIBAPS, Polyra) and North America (Indiana Purdue University, University of British Columbia). PhD in Physics (Cum Laude) from University of Valencia (2001) MSc in Neuroscience and Biology of Behaviour from University of Seville (2004) Research focuses on cortical network dynamics, mesoscopic auditory cortex modeling, top-down modulation in cognition, and affective computing in VR. His work combines nonlinear time series analysis with neurodynamic modeling to study spontaneous and task-related brain activity states. Recent publications demonstrate expertise in causal inference, neuroevolutionary optimization, and affective state detection using EMG/PPG sensors in VR environments. Collaborators include neuroscience labs (Sanchez-Vives, Durstewitz) and tech companies (Sony London, Emteq). PhD thesis award - Culture Institute 'Juan Gil-Albert', Spain (2001) Senior Fellow Higher Education Academy (2020) Secured major grants including Royal Society funding for cortical network modeling (2022), Human Brain Project ERC support for neuromorphic hardware (2022), and Santander Bank research travel grants (2016). Supervised 14 PhD students through projects involving data streams, VR immersion, and cortical dynamics. Labs include Balaguer Lab (GitHub), Interdisciplinary Neuroscience Research Centre, and collaborations with Human Brain Project. Teaching encompasses doctoral-level neuroscience, MSc data analytics, and undergraduate systems design courses.

• Distributed Systems
• Blockchain Technology
• Graph Neural Networks
• +5 more

Hans-Arno Jacobsen is a Professor at Technische Universität München (Faculty of Computer Science, Germany) and the University of Toronto (Department of Electrical and Computer Engineering, Canada). His research spans distributed systems, blockchain technology, and machine learning for energy systems. Research Interests : Blockchain consensus algorithms, federated learning, graph neural networks, quantum computing applications, and energy-efficient distributed systems. Publication Trends : Recent work focuses on decentralized consensus in blockchains, energy-aware language model inferencing, quantum chemistry simulations, and graph neural network scalability. Collaborations : Regularly works with Ruben Mayer, Shashank Motepalli, and Gengrui Zhang on blockchain and machine learning projects.

Mohsen AminiView profile

Associate Professor

• Cloud Computing
• Artificial Intelligence (AI)
• Domain-Specific Cloud Computing
• +7 more

Mohsen Amini is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas (UNT). He previously held tenured positions at the University of Louisiana Lafayette (UL Lafayette) and completed postdoctoral research at the University of Miami and Colorado State University. His research focuses on Cloud 2.0 systems, AI-friendly serverless architectures, and domain-specific computing for Industry 4.0, multimedia, and edge-to-cloud integration. He leads the High Performance Cloud Computing (HPCC) Lab and has attracted over $2.7 million in research funding, including an NSF CAREER Award (2021). His work emphasizes democratizing cloud-native development and enhancing system resilience through heterogeneous computing frameworks. Education: Ph.D. in Computing and Software Engineering, University of Melbourne (2012) M.Sc. in Software Engineering, Ferdowsi University (2006) B.Sc. in Software Engineering, Azad University (2003) Research Interests: Cloud+AI Integration Fluid Computing across Edge-to-Cloud Continuum Domain-Specific Cloud Platforms (e.g., Industry 4.0, Multimedia) Confidential Computing Security Awards: NSF CAREER Award (2021) Best Service Award at IEEE/ACM CCGrid 2023 Francis Patrick Clark/BORSF Endowed Professorship (2021–2024) Grants & Projects: NSF IRES Track 1: Wireless Federated Fog Computing for Remote Industry 4.0 ($400k grant) NSF CAREER: Special-Purpose Cloud for Serverless Multimedia Streaming ($513k grant) SmartSight: AI-Based Platform for Visually Impaired Assistance ($499k grant) Teaching & Advising: Current advising 5 PhD students and leading a team of 25+ researchers Teaches courses on Cloud Computing, Distributed Systems, and Software Engineering Lab & Collaborations: HPCC Lab develops open-source tools like the E2C simulator and OaaS framework. Active collaborations with institutions like IMDEA Networks and industry partners like Hub Enterprise Inc.

• Database Systems
• Data Engineering
• Distributed Systems
• +4 more

Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.

Ali JannesariView profile

Associate Professor

• High-Performance Computing
• Machine Learning
• Software Analytics
• +5 more

Ali Jannesari is an Associate Professor and Director of the Laboratory for Software Analytics and Pervasive Parallelism (SwAPP Lab) at the Department of Computer Science, Iowa State University. His research focuses on the intersection of High-Performance Computing (HPC) and AI, aiming to advance reliable and efficient software for modern parallel computing platforms. He has held academic and research roles including Senior Research Fellow at UC Berkeley and leadership positions in Germany's Technical University of Darmstadt and RWTH Aachen University. He holds a Habilitation in Computer Science (2016, TU Darmstadt), PhD (2010, KIT), and M.S. (2005, University of Stuttgart). Education: Habilitation in Computer Science, Technical University of Darmstadt, Germany (2016) Ph.D. in Computer Science, Karlsruhe Institute of Technology (2010) M.S. in Computer Science, University of Stuttgart (2005) Research Interests: High-Performance Computing (HPC), Machine Learning, Parallel Computing, Software Analytics, AI-driven compiler optimization, federated learning, and cross-language code analysis. His work bridges HPC and AI to enhance computational efficiency and scalability in data-driven applications. Recent Contributions: Leading research in GNN-based code parallelization (AutoParLLM), federated multimodal learning, and HPC performance optimization via graph-based methods. His lab's work has been published in top venues like NAACL, ICS, and NeurIPS. Lab & Team: The SwAPP Lab develops tools for software analytics and pervasive parallelism, with a focus on HPC-AI integration. Current projects include optimizing distributed training systems and improving code migration with LLMs.

Yang LiView profile

Assistant Professor

• Computer Architecture
• Machine Learning
• On-Device AI
• +4 more

Yang Li is an Assistant Professor of Computer Science at Iowa State University, specializing in computer architecture, machine learning, and their intersection. He holds a Ph.D. and M.S. from Carnegie Mellon University (2020), an M.S.E. from the University of Texas at Austin (2013), and a B.E. from Tsinghua University (2011). Prior to academia, he worked as a Senior Research Scientist at Meta, a Research Scientist at Meta, and a Researcher at Microsoft. His research focuses on large language models (LLM) acceleration, on-device AI, cloud infrastructure optimization, and spatiotemporal forecasting. He has contributed to over 20 peer-reviewed publications at top venues like ASPLOS, EMNLP, and ICASSP. Research Interests: Algorithmic and systems-level acceleration of LLMs On-device AI co-design and privacy Cloud memory/power management Graph-based spatiotemporal forecasting Teaching: Taught COMS 6730 (Advanced Topics in ML), COM S 321 (Computer Architecture), and guest-lectured on graph signal processing. Recent teaching scores include 4.75/5.0 (Fall 2024) and 4.67/5.0 (Spring 2025). Awards: IBM Patent Application Award (2021) and multiple patents on power management systems for data centers. Service: Program committee member for DAC 2024, NeurIPS 2024, and ICLR 2025. Reviewer for ACM TACO, IEEE TPAMI, TPDS, and others.

Ben DuanView profile

Academic

• Reinforcement Learning
• Graph Neural Networks
• Intelligent Transportation Systems (ITS)
• +4 more

Ben Duan is an Adjunct Lecturer at the Department of Data Science & AI, Faculty of Information Technology, Monash University. He holds a PhD in Big Data and Electronics from the University of Technology Sydney. Previously, he was a Research Fellow at the Hong Kong University of Science and Technology. His research focuses on reinforcement learning, graph neural networks, intelligent transportation systems (ITS), and fintech. He teaches courses such as FIT5221: Intelligent Image and Video Analysis, FIT5215: Deep Learning, and FIT5217: Statistical Data Modeling at Monash’s Suzhou campus. Dr. Duan’s work aligns with UN Sustainable Development Goals related to sustainable cities and communities (SDG 11) and industry innovation (SDG 9). His recent research explores AI-driven solutions for traffic congestion, stock trend prediction using graph neural networks, and spiking neural networks for neural activation control. He has contributed to over 28 publications and a patent with the Monash Suzhou Research Institute. He supervises PhD students interested in reinforcement learning, graph neural networks, or ITS, offering full scholarships at Monash Suzhou. Key collaborations span transportation systems, fintech, and data-driven industrial processes.

Bettina SchaefliView profile

Assistant Professor

• Hydrology
• Climate Change
• Water Resources Management
• +3 more

Bettina Schaefli is an Assistant Professor and Unit Leader of Hydrology at the Institute of Geography, University of Bern. Her work focuses on quantifying and predicting hydrological processes to support sustainable water resource management and mitigate extreme events. She holds a PhD in Hydrology from EPFL (2005), followed by postdoctoral research at the University of Potsdam and University of Illinois at Urbana-Champaign, and served as an Assistant Professor at TU Delft (Netherlands). Since 2016, she has been an SNSF Assistant Professor at the University of Lausanne before joining the University of Bern. Education: Environmental Engineering (EPFL, 2005 PhD) Research Priorities: Glacier-influenced hydrology, climate change impacts on hydropower, and open science initiatives Leadership Roles: Editor of Hydrology and Earth System Sciences , President of the Swiss Hydrological Commission Her research investigates how hydrological systems respond to climate change, particularly in mountainous regions. Recent studies include modeling snow and ice melt dynamics, groundwater connectivity in cryosphere systems, and future hydropower potentials in Switzerland. She collaborates with the Swiss Competence Center for Energy Research to address water resource sustainability. Key contributions include the Northern Hemisphere Snow Water Equivalent (NH-SWE) dataset, which enhances understanding of snowmelt patterns, and studies on proglacial catchment hydrodynamics. She advocates for open science through data sharing and accessible methodologies. Grants/Funding: Swiss National Science Foundation (SNSF) career development projects, institutional research grants. Labs/Teams: Leading the Hydrology Unit at the University of Bern’s Institute of Geography, collaborating with international research networks on mountain hydrology and climate change.

• Analogue and Mixed Signal IC Design
• Digital System Design (FPGA)
• CMOS Lab-on-Chip Platforms
• +4 more

Junming Zeng is a Researcher at the Department of Electrical and Electronic Engineering, Faculty of Engineering at Imperial College London. His work focuses on advanced CMOS technologies for biomedical applications, including lab-on-chip platforms and ion imaging systems. He holds a PhD from Imperial College London (2022), following a Master's in Analogue and Digital Integrated Circuit Design (2017) and a Bachelor's in Electronic Engineering (2016), both from UK institutions. His research interests span analogue/mixed-signal IC design, FPGA-based digital systems, and ultra-high-speed ion sensing solutions. He has pioneered CMOS lab-on-chip platforms for real-time chemical monitoring and developed compressed sensing techniques for optimizing sensor array performance. His work integrates deep learning for applications like diabetes glucose prediction and drift compensation in ISFET sensors. Zeng has received the Best Student Paper Award (1st Prize) at ISCAS 2018 and Imperial's Department PhD Scholarship. His research bridges electrical engineering and biomedical engineering, with a focus on scalable, energy-efficient systems for healthcare and diagnostics. He leads projects involving edge computing, temporal fusion transformers, and microfluidic integration, demonstrating expertise in both hardware innovation and algorithmic development. His lab develops cutting-edge systems such as 1000fps ISFET SoCs with programmable gain and high-throughput digital readout architectures. Recent work includes live demonstrations of real-time pH monitoring in 3D-printed microfluidic systems and spatio-temporal ion membrane characterization platforms.