Find Academic Supervisors

Suyash GuptaView profile

Assistant Professor

• Distributed Systems
• Databases
• Blockchain
• +6 more

Suyash Gupta is a Tenure-Track Assistant Professor in the Department of Computer Science at the University of Oregon, where he leads the Distopia Laboratory and co-leads the Oregon Networking Research Group. His expertise lies in distributed systems, databases, blockchain technologies, fault tolerance, and federated learning. Education: Ph.D. in Computer Science, University of California, Davis (2022) M.S. in Computer Science, Purdue University (2017) M.S. (Research) in Computer Science, Indian Institute of Technology Madras Research Focus: Dr. Gupta’s research is centered on designing efficient distributed, decentralized, and blockchain systems that are resilient to arbitrary failures and can scale across wide-area networks. His work spans consensus protocols, Byzantine fault tolerance, secure transaction processing, and federated learning systems. He has contributed foundational work in permissioned blockchain architectures and fault-tolerant distributed databases. Scientific Contributions & Awards: Best Paper Award, EuroSys 2023 Distinguished Reviewer Award, SIGMOD 2025 Best Graduate Researcher Award, UC Davis Author of Fault-Tolerant Distributed Transactions on Blockchain , Morgan & Claypool Teaching & Mentorship: He currently teaches advanced courses like CS 607: Hot Topics in Systems and CS 451/551: Database Processing . He actively mentors a diverse group of PhD and MS students, including Nihal Balivada, Shistata Subedi, Neil Sharma, and others from institutions like UC Davis and BITS Pilani. Labs & Teams: Dr. Gupta leads the Distopia Laboratory at UO and co-leads the Oregon Networking Research Group , both focused on cutting-edge research in distributed systems and secure networked architectures.

Nour MoustafaView profile

Associate Professor

• Cyber Security
• Computer System Security
• Networking and Communications
• +7 more

Dr. Nour Moustafa is an Associate Professor and ARC DECRA Fellow at the School of Systems & Computing (SysCom) , University of New South Wales (UNSW) Canberra , Australia. He leads the Intelligent Security Group and focuses on developing AI/ML-driven cybersecurity frameworks for smart systems. Educated at Helwan University (BSc/MSc in Information Systems) and UNSW (PhD in Cybersecurity). Research Interests include intrusion detection, threat intelligence, privacy preservation, digital forensics, and cyber resilience, with methodologies spanning statistical analysis , machine learning , and deep learning applied to IoT , Edge/Cloud , and Industrial IoT environments. His work emphasizes federated learning for privacy preservation, blockchain for secure AI, and digital twins for network self-healing. Notable contributions include the TON-IoT , Bot-IoT , and UNSW-NB15 datasets for cybersecurity evaluation. Scientific Awards : 2020 Spitfire Memorial Defence Fellowship ACM Distinguished Speaker IEEE Senior Member He has served as guest associate editor for IEEE Transactions journals and held leadership roles in conferences like IEEE TrustCom . His research bridges academia and industry, with over 75 publications in top-tier venues.

• Energy Efficiency in Communication Networks
• Quality of Service (QoS) and Quality of Experience (QoE)
• Network and Service Management
• +5 more

Dr. Frank Loh is a researcher at the Department of Computer Science III, University of Würzburg, specializing in energy efficiency, network performance, and Quality of Experience (QoE) in communication networks. His work focuses on optimizing LoRaWAN deployments, serverless computing, and edge-cloud environments, with an emphasis on reducing message collisions and improving resource utilization. He actively contributes to methodologies for gateway placement, traffic modeling, and energy consumption metrics. Research Areas Energy Efficiency in Communication Networks Quality of Service (QoS) and Quality of Experience (QoE) LoRaWAN Network Planning Edge and Serverless Computing Network Resource Analysis Recent Publications 2025: Energy modeling for 6G base stations 2025: Server cluster resilience via Markov models 2024: Serverless computing in edge-cloud environments 2024: LoRaWAN channel access optimization

Lin CaiView profile

Associate Professor

• Wireless Communication
• Networking
• Green Radio Communication Networks
• +5 more

Lin Cai is an Associate Professor in the Department of Electrical and Computer Engineering at Illinois Institute of Technology, serving as Director of Graduate Affairs. She holds a Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Canada (2010). Her research focuses on sustainable wireless communication and networking, particularly in energy harvesting, IoT, and deep reinforcement learning for resource management. Key research projects include energy sustainability in renewable-energy-powered networks, energy-efficient 5G protocols, and radio resource management over unlicensed bands. She has received the NSF CAREER Award (2016-2020), Best Paper Awards, and NSERC Postdoctoral Fellowship. Her work spans theoretical frameworks and practical implementations in HetNets, MIMO, and UAV-based systems. Education: Ph.D., E&CE, University of Waterloo, Canada (2010) Affiliations: Executive Editorial Committee member, IEEE Transactions on Wireless Communications (2021–present); Associate Editor for multiple IEEE journals Teaching: Courses include ECE 407/408 (Computer Networks) and ECE 517 (Modern Wireless Network Protocols) Her research emphasizes cross-layer protocol design, sustainable capacity planning, and topology control. Notable contributions include frameworks for energy sustainable radio resource management and optimization in dense HetNets. She actively contributes to conference organization and technical program committees for IEEE INFOCOM, GLOBECOM, and ICC.

Feng YanView profile

Associate Professor

• Big Data
• Machine Learning
• Systems
• +6 more

Dr. Feng Yan is an Associate Professor at the University of Houston's Computer Science Department, leading the Intelligent Data and Systems Lab (IDS Lab). He previously held an Associate Professor position at the University of Nevada, Reno. His research focuses on bridging Big Data, Machine Learning, and Systems, with interdisciplinary applications in wildfire science, materials engineering, and civil infrastructure. He has received prestigious awards such as the NSF CAREER Award and the Regents' Rising Researcher Award. Education: Ph.D. (2016) and M.S. (2011) in Computer Science from College of William and Mary; B.S. (2008) in Computer Science from Northeastern University. Research experience includes roles at Microsoft Research and HP Labs. Research Interests: Large Language Models (LLM), Distributed Deep Learning, AutoML, Serverless Computing, Federated Learning, and AI-driven domain sciences. His work emphasizes real-world impact through collaborations with industry and national labs. Publications: Over 60+ papers in top-tier venues like NeurIPS, ICLR, KDD, AAAI, SOSP, SC, and VLDB. Key contributions include ZeRO++ (collective communication optimization), Gradient Compression techniques, and Federated Learning frameworks like TiFL and HDFL. Awards: NSF EPSCoR Award ($20M), NSF CAREER Award, FAA BAKFAA Grant, and multiple best paper awards (IEEE CLOUD 2018, CLOUD 2019). Active in program committees for HPDC, ICAC, ICPE, and AAAI. Advising: Supervised over 30+ graduate/undergraduate students, with placements at Microsoft Research, IBM, Oak Ridge National Lab, Facebook, and MathWorks. Runs a vibrant lab with a focus on interdisciplinary AI/Systems research.

• Database Systems
• Distributed Databases
• Transaction Processing
• +5 more

Daniel J. Abadi is a prominent researcher in database systems at Yale University. With over two decades of impactful research, he has made significant contributions to the fields of distributed databases, transaction processing, and column-oriented database systems. His work bridges theoretical foundations with practical implementations that have influenced both academia and industry. Dr. Abadi's research primarily focuses on database system architecture, with particular emphasis on: Distributed and geo-replicated database systems High-performance transaction processing Column-oriented and analytical database systems Stream processing and real-time analytics Cloud and serverless database technologies Integration of machine learning with database systems His recent work shows a continued focus on addressing scalability challenges in modern database systems, with particular attention to multi-region transaction processing, automated data management, and the integration of machine learning techniques. The trend in his publications indicates a strong emphasis on practical, deployable systems that solve real-world problems faced by industry. Dr. Abadi has been instrumental in several major research initiatives and reports that have shaped the direction of database research, including the Seattle Report and the Cambridge Report on Database Research. Throughout his career, Dr. Abadi has mentored numerous students and collaborated extensively with leading researchers in the field. His work has received significant recognition through widespread citations and adoption of his ideas in both academic and industrial database systems.

Soteris DemetriouView profile

Senior Lecturer

• Computer System Security
• Cyber-Physical Systems Security
• Machine Learning Security
• +3 more

Soteris Demetriou is a Senior Lecturer of Computer Systems Security at Imperial College London's Department of Computing, within the Faculty of Engineering. He leads the Applications, Platforms, and Systems Security (APSS) Research Lab and directs the Academic Centre of Excellence in Cyber Security Research (ACE-CSR). His research focuses on securing mobile, IoT, and cyber-physical systems through techniques like explainable AI, reverse engineering, and trusted computing. Notable contributions include tools for privacy preservation in machine learning models, detection of LiDAR spoofing attacks, and securing Android's middleware. Education: PhD and MSc in Computer Science (University of Illinois at Urbana-Champaign), Diploma in Electrical and Computer Engineering (University of Patras). Research Interests: Mobile/IoT security, AI security, trusted computing, and vulnerability analysis. Key areas include privacy in generative models, adversarial attacks on autonomous systems, and large-scale distributed systems. Publications: Over 50 peer-reviewed papers in top venues like NDSS, CCS, and SOSP. Recent work addresses privacy in speech generation, LiDAR security for autonomous vehicles, and hyperscale serverless architectures at Meta. Awards: Distinguished Paper Award at NDSS 2018, Best Paper at SafeThings 2024, and multiple travel grants. Served on technical committees for PETS, CCS, and AutoSec. Grants & Collaborations: SPRITE+ grant for Bio-IoT security, collaboration with Meta on distributed systems, and leadership in ACE-CSR. Labs: APSS Lab focuses on systems and AI security, with interdisciplinary projects in healthcare and autonomous systems.

Tijani CHAHEDView profile

Professor

• Network Optimization
• Edge Computing
• Machine Learning in Networks
• +4 more

Prof. Tijani CHAHED is a Professor at Telecom SudParis, part of Université Paris-Saclay, affiliated with the SAMOVAR laboratory and the NeSS research group. His work focuses on network optimization, edge computing, machine learning applications in telecommunications, and game-theoretical frameworks for distributed systems. He holds a position in the Department of Computer Science and Telecommunications. His research spans resource allocation in 5G/6G networks, energy efficiency strategies for mobile infrastructure, reinforcement learning for dynamic systems, and coalitional game theory for multi-agent systems. Key contributions include optimization of cache allocation in edge computing, latency-critical traffic management (URLLC), and strategic investment models for distributed computing infrastructures. Selected articles highlight advances in edge computing resource management, metaverse data transport over 5G, and energy-efficient sleep mode control for base stations. His work often intersects with industrial applications in green networks and smart grid integration for mobile infrastructure. Collaborations involve institutions like École Polytechnique, INRIA, and industry partners in telecommunications. Current projects include 6G network architectures, metaverse-enabled edge services, and decentralized resource allocation frameworks. Labs/Teams: SAMOVAR Lab (Signal and Media Access Networks, Optical and Radio Networks), NeSS Group (Networked Systems and Services).

• Control Technology
• Distributed Optimization
• Machine Learning
• +4 more

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

• Cloud Computing
• Data Center Optimization
• Resource Allocation
• +4 more

Bhuvan Urgaonkar is a Professor in the Department of Computer Science and Engineering at Penn State University's College of Engineering. His research centers on optimizing cloud computing systems through innovative approaches to resource allocation, cost efficiency, and energy management. Current research focuses on Burstable Instance Scaling Serverless Computing Optimization Distributed Storage Systems Multi-resource Fair Allocation Cloud Economics Recent publications highlight advancements in autoscaling techniques, serverless architecture design, and trace modeling for high-load scenarios. These works emphasize practical solutions for cost-effective resource utilization in public cloud environments. Scientific Awards: CNS: Core: Small: Consistent, Geo-Distributed Data Stores on the Public Cloud (NSF, 2022-2025) CNS Core: Small: Principled Methodologies for Automated Cost-Effective Service Blending (NSF, 2021-2024) PPoSS: Cross-Layer Design for HPC in the Cloud (NSF, 2020-2022) CSR: Burstable Instances for Cost-Efficacy (NSF, 2017-2020) CSR: Student Travel Support for SIGMETRICS (NSF, 2016-2017)

Sebastian AngelView profile

Researcher

• Distributed Systems
• Security
• Privacy
• +1 more

Sebastian Angel is affiliated with the University of Pennsylvania and Microsoft Research . His research spans Distributed Systems , Security , Privacy , and Proofs . Contributions: Personal Website Recent work includes: 2025 SPLASH: Structural temporal logic for mechanized program verification (keywords: Computer Science, Formal Verification; subfields: Temporal Logic, Program Verification). 2023 POPL: Executing Microservice Applications on Serverless, Correctly (keywords: Distributed Systems, Security; subfields: Serverless Computing, Microservices).

Monowar BhuyanView profile

Associate Professor

• Machine Learning
• Anomaly Detection
• Systems and AI Security
• +6 more

Monowar Bhuyan is an Associate Professor in the Department of Computing Science at Umeå University, Sweden, leading the Cyber Analytics and Learning Group within ADSLab. He holds a Ph.D. in Computer Science from Tezpur University and has held academic positions at Assam Kaziranga University and Umeå University. His research focuses on machine learning, anomaly detection, edge AI, and distributed systems security. He has secured over 35 MSEK in grants from WASP, STINT, and EU Horizon programs. Education Ph.D. in Computer Science and Engineering, Tezpur University (2014) M.Tech. in Information Technology, Tezpur University (2009) B.E. in Computer Science and Engineering, IETE (2007) Research Interests Distributed/Federated/Responsible Machine Learning Cybersecurity and Anomaly Detection in Edge Clouds AI for DDoS Defense and Cyber Resilience Edge AI and Serverless Computing Recent Contributions His recent work addresses secure federated learning, DDoS attack detection in cloud-edge systems, and responsible AI. Key publications include novel frameworks for VSI-DDoS detection and federated learning optimizations. Awards & Grants Best Paper Awards at ICONIP 2023 and ACM ICACCI 2012 WASP NEST Grant (AIR2 Project, 5 MSEK) EU Horizon Europe Grant (SovereignEdge.Cognit, 8.27 MSEK) Lab & Collaborations He leads the Cyber Analytics and Learning Group (ADSlab), collaborating with institutions like KTH, Linköping University, and Nara Institute of Science and Technology (NAIST). The lab focuses on AI-driven security solutions for distributed systems.

• Business Informatics
• Artificial Intelligence
• Business Intelligence
• +10 more

Prof. Dr. Harald Ritz serves as Professor of Practical Computer Science, especially Business Informatics, at the Technical University of Central Hesse (THM) within the Department of Mathematics, Natural Sciences and Computer Science since 2003. He holds leadership roles as Chair of Examination Committees for B.Sc. and M.Sc. Business Information Systems and Spokesperson for the MNI department in the Business Informatics Working Group (AKWI). His educational background includes a Diplom in Business Informatics (Dipl.-Wirtsch.-Inform.) and doctorate (Dr. rer. pol.) from the Technical University of Darmstadt, following professional experience at SAP SI AG and a professorship at Heilbronn University of Applied Sciences. Ritz's research centers on AI-driven digital transformation for data-driven enterprises, with focus on the “Data to Decision” value chain encompassing Framing, Allocation, Analytics, and Preparation phases. His work integrates business intelligence, data warehousing, machine learning, and SAP ecosystems to address challenges in SME digitalization, operational IT management, and educational technology. Current projects emphasize AI applications in higher education, including intelligent tutoring systems and automated feedback mechanisms. Analysis of his 15 most recent publications reveals a consistent trajectory toward applied AI solutions in business contexts, particularly in intelligent chatbots for educational support, financial trading algorithms, and cloud-based data infrastructure. The research demonstrates increasing integration of no-code platforms, real-time analytics, and domain-specific AI applications across logistics, banking, and procurement sectors. No scientific awards were documented in the source materials. Professor Ritz actively supervises academic development through bachelor’s and master’s theses, doctoral research, and collaborative projects. Current initiatives include the “Winfy” AI chatbot (v4.0, 2025), AI-based feedback systems for educational content (Freiraum 2025 grant), the frits intelligent tutoring project with Prof. Kammer, and doctoral research on AI adoption in SMEs. His work bridges theoretical research with practical implementation in SAP environments and cloud platforms. He operates within THM’s MNI department infrastructure, collaborating through the Business Informatics Working Group (AKWI) and contributing to the Digital Classroom communication platform for online education.

Luo MaiView profile

Assistant Professor

• Computer Systems
• Machine Learning Systems
• Data Management
• +3 more

Luo Mai is an Assistant Professor at the University of Edinburgh's School of Informatics , with an upcoming promotion to Associate Professor (UK Reader) in August 2025. He leads the Large-Scale Machine Learning Systems Group and co-leads the UK EPSRC Centre for Doctoral Training in Machine Learning Systems and an ARIA Project on Scaling AI Compute by 1000X . PhD in Computer Science (Imperial College London, 2018) MRes in Advanced Computing (Imperial College London, 2012) His research focuses on the intersection of computer systems , machine learning , and data management . Key contributions include award-winning systems like WaferLLM (wafer-scale LLM inference), Tenplex (elastic ML), and ServerlessLLM (serverless LLM serving), published at top venues (OSDI, SOSP, ICML, NeurIPS, JMLR). Recent publications demonstrate trends in GPU-based distributed systems , LLM optimization , and adaptive machine learning . His team has developed groundbreaking open-source projects including TensorLayer , TorchOpt , and ServerlessLLM . Awarded Microsoft Research StarTrack Scholar (2024) , secured ARIA grant (2024) with Imperial College & Cambridge University, and received Google Fellowship during PhD (2012-2016). As an educator, he designed Edinburgh's popular Machine Learning Systems course (150+ students). His group supervises multiple PhD students including Yao Fu (recognized as 2024 Rising Star in ML & Systems) and Leyang Xue .

Al Amjad Tawfiq IsstaifView profile

Researcher

• Cloud and Edge Computing
• Microservices and Serverless
• Operating Systems and Middleware
• +5 more

Al Amjad Tawfiq Isstaif is a Research Associate in the Systems Research Group (SRG) at the Department of Computer Science and Technology (also known as "The Computer Lab") at the University of Cambridge, working with Professor Richard Mortier. He is a member of Clare College. In 2018, he was awarded the Said Foundation Cambridge Scholarship to study the MPhil in Advanced Computer Science (ACS) at the Computer Lab. Before moving to Cambridge, he co-founded several projects for empowering engineering students in Syria, including the award-winning education enterprise: Wikilogia. His research interests include: Cloud and Edge Computing Microservices and Serverless Operating Systems and Middleware Networked and Distributed Systems Machine Learning Systems Human-Centred Computing Machine Learning and Artificial Intelligence Systems and Networking Isstaif's primary research focus is on building self-scaling network services on top of next generation infrastructure (IoT/5G/edge), leveraging lightweight virtualization techniques and distributed tracing. His work falls under the Compute First Networking research project. He has deep passion for social entrepreneurship and the role of digital infrastructure in addressing development and sustainability challenges across the globe. His technical expertise bridges the IT and telecom worlds, with emphasis on cloud-native and open source movements shaping future infrastructure. His recent publications demonstrate a strong focus on optimizing serverless computing environments, with particular attention to scheduling algorithms, resource management, and latency optimization in Linux-based systems. The trajectory of his work shows progression from database performance modeling to cutting-edge research in edge computing and network infrastructure. Notable awards and recognitions: Said Foundation Cambridge Scholarship (2018) Co-founded Wikilogia, an award-winning education enterprise for engineering students in Syria Isstaif has teaching experience in Operating Systems (LT 2021) and maintains an active research profile within the Systems Research Group. His work connects theoretical computer science with practical applications in cloud infrastructure, particularly focusing on how these technologies can serve broader societal needs in developing contexts. He is located in Room FN07 at the William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD.