Find Academic Supervisors

• Software Engineering
• Artificial Intelligence
• Cybersecurity in AI
• +5 more

Amin Alipour is an Associate Professor in the Department of Computer Science at the University of Houston, where he directs the Software Engineering Research Group. He holds a Ph.D. from Oregon State University and multiple advanced degrees from institutions in Iran and the U.S. His work focuses on improving safety and user experience of AI-assisted software engineering tools, with funding from NSF and IARPA. Prior to joining the U.S., he taught at the University of Kashan and Shiraz University in Iran. Education: Ph.D. in Computer Science, Oregon State University (Advisor: Alex Groce) M.S. in Computer Science, Michigan Technological University M.S. in Computer Engineering, Tarbiat Modares University B.S. in Computer Engineering, Petroleum University of Technology (Iran) Research interests include: Secure AI systems, particularly in code LLMs Trojan detection and mitigation in neural models Educational impacts of AI tools in programming education Formal methods for software verification Human-AI collaboration in programming tasks His recent articles explore cybersecurity in AI, student interactions with LLMs, and adversarial attacks on code models. He currently serves on program committees for ICER 2025 and ICSME 2025. His research group develops tools like FeatureExtractor and ProgramTransformer for analyzing neural code intelligence models.

Yuan YuanView profile

Assistant Professor

• Optoelectronic Devices
• Single-Photon Detection
• Silicon Photonics
• +3 more

Yuan Yuan is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University, based at the Oakland, California campus. His research focuses on optoelectronic devices, single-photon detection, silicon photonics, and large-scale photonic integrated circuits for interconnects and computing. He joined Northeastern in January 2025 and is affiliated with the Institute for NanoSystems Innovation. Yuan holds a PhD in Electrical Engineering from the University of Virginia (2019). His academic affiliations include IEEE and Optica. His work emphasizes practical applications of photonics in high-speed communication and computing systems, leveraging silicon-based photonic integration and advanced device designs. Recent publications highlight innovations in avalanche photodiodes, microring modulators, and precision optical neural networks. His research bridges fundamental photonics principles with scalable manufacturing processes. No scientific awards are explicitly mentioned in the provided text. Yuan’s advising and grant activities are not detailed, though his lab focuses on cutting-edge photonic systems. He collaborates with industry-standard foundry processes to develop next-generation photonic integrated circuits.

Yury PolyanskiyView profile

Professor

• Information Theory
• Machine Learning
• Statistics
• +3 more

Yury Polyanskiy is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), affiliated with the Laboratory for Information and Decision Systems (LIDS), the Institute for Data, Systems, and Society (IDSS), and the MIT Statistics and Data Science Center. He holds a Ph.D. from Princeton University (2010) and an M.S. from the Moscow Institute of Physics and Technology (2005). His research focuses on information theory, machine learning, statistical inference, error-correcting codes, and wireless communication. He has contributed to fundamental limits of communication systems, finite-blocklength analysis, and applications of information theory to learning and signal processing. Notable awards include the 2020 IEEE Information Theory Society James Massey Award, the 2013 NSF CAREER Award, and the 2011 IEEE Information Theory Society Paper Award. His work spans theoretical advancements and practical applications, including the development of the SPECTRE toolbox for short-packet communication. He is also co-authoring a textbook on information theory. Recent research highlights include studies on quantization techniques for machine learning (e.g., NestQuant), transformer-based empirical Bayes methods, and novel approaches to massive random access in wireless networks (e.g., unsourced multiple access). His contributions bridge information theory and modern data science, addressing challenges in high-dimensional data representation, neural network dynamics, and efficient communication architectures.

• Computer Architecture
• Computer Systems
• Compilers
• +8 more

José Cano Reyes is a Senior Lecturer (Associate Professor) at the University of Glasgow's School of Computing Science, leading the Glasgow Intelligent Computing Lab (gicLAB) and serving as deputy Head of the GLAsgow Systems Section (GLASS). His academic career includes postdoctoral roles at the University of Edinburgh (2014-2018) and Universitat Politècnica de Catalunya (2012-2013), with a PhD and engineering degree from Universitat Politècnica de Valencia (2004-2012). He has held visiting and guest lecturer positions at Edinburgh and Glasgow across computer architecture, compilers, and embedded systems topics. Research focuses on hardware-software co-design for edge AI, including DNN acceleration (FPGA/GPU), encrypted AI systems, and secure mission-critical SoCs. Key projects include EU's dAIEDGE, EPSRC IDEAL, and UKRI AppControl. He leads over 15 research staff and students in areas like quantization, sparsity exploitation, and robust AI deployment. Notable contributions span 100+ peer-reviewed publications across top venues (ISCA, IJCNN, IEEE TPDS) and 3 authored books on ad hoc networks and embedded systems. Academic service includes organizing 20+ conferences (ISPASS, Euro-Par, ASPLOS) and serving on editorial boards for ACM TACO and IEEE TPDS. His educational efforts include teaching Computer Architecture (Year 4), Computer Systems (Year 1), and supervising over 20 PhD/MSc students since 2017.

• Computer Architecture
• Edge AI Systems
• Memory Systems
• +6 more

Rafael Medina Morillas is a researcher at the Embedded Systems Laboratory (ESL) at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he focuses on computer architecture and hardware acceleration for edge AI systems. His research addresses the memory wall problem through innovative architectural designs that improve energy efficiency and performance in data-intensive applications. His primary research interests include: Compute-near-Memory architectures Hardware acceleration for machine learning Edge AI systems Chiplet architectures and interconnects Wireless communication for computing systems Medina Morillas' publication record demonstrates significant advancements in memory systems and hardware acceleration. His work on SideDRAM shows up to 83% EDAP reduction compared to state-of-the-art designs, while his research on wireless communication achieves up to 2.64x speedup for deep neural networks. His recent publications focus on structured pruning techniques for transformers, co-design frameworks for edge AI, and thermal management solutions for heterogeneous systems. His research is supported by collaborations with IMEC, Université de Bordeaux, and HEIG-VD, as well as funding from EC H2020 projects and the ACCESS-AI Chip Center. These partnerships enable comprehensive exploration of architectural innovations across different technology domains. As evidenced by his doctoral thesis 'System-aware Architectural Co-design to Tackle the Memory Wall,' Medina Morillas takes a cross-layer approach to system design, integrating hardware and software optimizations to address fundamental bottlenecks in modern computing systems. His work demonstrates how system-aware architectural design can achieve improvements in runtime, energy consumption, and thermal behavior for data-intensive applications.

• Computer Architecture
• Edge Artificial Intelligence
• Hardware Acceleration
• +4 more

Pengbo Yu is a researcher at the Embedded Systems Laboratory (ESL) within the School of Engineering at École polytechnique fédérale de Lausanne (EPFL). His work focuses on developing energy-efficient hardware architectures for edge artificial intelligence applications, with particular emphasis on variable-precision computing techniques. His research interests span computer architecture, edge AI hardware acceleration, neural network quantization, memory systems, and low-power computing. Dr. Yu's work bridges the gap between algorithmic robustness in quantized neural networks and specialized hardware implementations that can dynamically adjust precision to maximize efficiency in resource-constrained environments. His recent publications demonstrate a strong focus on the SoftSIMD paradigm, Dynamic Bitwidth-Frequency Scaling techniques, and near-memory computing solutions that achieve significant energy savings (up to 67%) compared to state-of-the-art approaches. His work shows consistent output in top venues including IEEE Transactions on VLSI Systems and ACM Transactions on Embedded Computing Systems. Dr. Yu's research is supported by multiple funding agencies including EC H2020, H2020, and SNSF. He maintains active collaborations with researchers across Europe, including institutions like IMEC and National Technical University of Athens. His laboratory work centers around the Embedded Systems Laboratory at EPFL, where he develops and validates novel hardware architectures for energy-efficient AI acceleration. Current projects include silicon validation of his proposed architectures on open-source RISC-V platforms and integration of variable-precision computing techniques into memory systems.

• Wireless Communication Engineering
• Artificial Intelligence
• Neural Network
• +5 more

Benaoumeur Senouci is an Associate Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark (SDU). His research spans interdisciplinary domains including wireless communication, artificial intelligence, and embedded systems. Research Focus: Wireless Communication Engineering, Neural Networks, and Edge Computing Key Collaborations: Active partnerships in 6G wireless systems, autonomous vehicle efficiency, and precision agriculture technologies Recent work highlights machine learning applications in fuel consumption estimation (2025), hybrid beamforming for 6G networks (2025), FPGA-based neural network prototyping (2024), and smart agriculture control systems (2024). Research outputs demonstrate strong integration of AI techniques with mechanical/electrical engineering challenges.

Thomas VillmannView profile

Professor

• Computational Intelligence
• Machine Learning
• Vector Quantization
• +7 more

Thomas Villmann is a Professor of Computational Intelligence and Techno-Mathematics at Mittweida University of Applied Sciences in Germany. He serves as Deputy Spokesperson for the Mathematics Department, AI Coordinator at the university, Director of the Saxon Institute for Computational Intelligence and Machine Learning (SICIM), and President of the German Chapter of the European Neural Network Society (GNNS). His academic credentials include the German 'Dr. rer. nat. habil.' designation, indicating both doctoral and habilitation qualifications in natural sciences. Professor Villmann's research focuses on computational intelligence with particular emphasis on vector quantization, learning vector quantization, neural networks, and interpretable machine learning. His work spans theoretical developments in mathematical foundations of machine learning algorithms as well as practical applications in bioinformatics, remote sensing, medical diagnostics, and autonomous systems. He has developed mathematically sound methods for data-based analysis (clustering), decision support systems, data-based prediction models, and visualization of complex data. His recent publication record demonstrates a strong trend toward interpretable and explainable AI, with emphasis on vector quantization techniques applied across diverse domains including medical diagnostics (particularly breast cancer detection), fairness in machine learning, satellite remote sensing, and autonomous vehicle systems. Villmann's work consistently bridges theoretical mathematics with practical applications, maintaining a focus on making machine learning models more transparent, reliable, and ethically sound. As Director of SICIM and leader of the Computational Intelligence Research Group, Professor Villmann oversees an integrated research ecosystem focused on problem-oriented intelligent data analysis. His institutes aim to stimulate interest in computational intelligence among students and young researchers while supporting public institutions, authorities, and companies in data analysis both regionally and internationally. His leadership extends to organizing academic events and workshops, including serving as president of the German Chapter of the European Neural Network Society.

Armin AlaghiView profile

Assistant Professor

• Embedded Systems
• Digital Circuits
• Mathematics
• +7 more

Armin Alaghi serves as a Research Scientist at Oculus Research (Redmond, WA) and holds an Affiliate Assistant Professor position at the University of Washington. His dual affiliation enables valuable knowledge transfer between cutting-edge industrial research and academic pursuits in computer systems engineering. Dr. Alaghi's research spans the intersection of embedded systems, digital circuits, and mathematics. His primary focus involves building low-power augmented reality (AR) and virtual reality (VR) systems while developing novel computation methods for unreliable beyond-CMOS technologies. His previous research contributions include significant work in stochastic computing (where he developed the STRAUSS synthesis methodology), reliable Network on chip (NoC) design, FPGA testing methodologies, NoC testing techniques, artificial neural networks implementations, asynchronous circuit design, and multi-valued logic systems. He has made his spectral-transform-based synthesis tool publicly available on GitHub, demonstrating commitment to open research. Analysis of Dr. Alaghi's publication record reveals a clear research trajectory from foundational circuit-level work toward practical applications in AR/VR systems. His publications from 2020-2025 demonstrate expertise spanning computer architecture, security for immersive technologies, neural network compression techniques, and homomorphic encryption methods. A recurring theme throughout his work is the exploration of quality-energy tradeoffs and error-resilient computing approaches, with increasing focus on security aspects of AR/VR systems in his most recent work. Dr. Alaghi maintains active connections with the broader research community, as evidenced by his Erdős number of 3 (Armin Alaghi John P. Hayes Frank Harary Paul Erdős) and his ongoing contributions to open-source research tools. His GitHub repository for stochastic computing synthesis shows community engagement with multiple contributors. At Oculus Research, Dr. Alaghi applies his theoretical expertise to practical challenges in next-generation AR/VR system development. His work bridges academic research with real-world product development, particularly in addressing energy efficiency challenges for wearable computing platforms through innovative circuit design approaches.

Guanhua ChenView profile

Assistant Professor

• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Applied Machine Learning
• +4 more

Dr. Guanhua Chen is an Assistant Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech). His research focuses on Natural Language Processing (NLP), Large Language Models (LLMs), and Multimodal AI, with applications in drug discovery and urban planning. He previously earned his Ph.D. from the University of Hong Kong and B.S./M.S. from Tsinghua University, with internships at Microsoft Research Asia and Huawei Noah’s Ark Lab. Education: Ph.D. in Computer Science (University of Hong Kong, 2022), B.S. and M.S. in Computer Science (Tsinghua University, 2012 & 2014) Dr. Chen's research explores LLMs, data synthesis, multimodal systems, and model compression. He leads a lab equipped with 16 RTX 4090 GPUs, 16 L40 GPUs, and 4 A100 GPUs, providing resources for students to experiment with open-source and proprietary LLM APIs. His recent publications highlight innovations in LLM jailbreaking, parameter-efficient tuning (MiLoRA), cross-lingual transfer (mCLIP), and evaluation frameworks for robustness testing. Notably, he was awarded the Microsoft Research Asia StarTrack Scholar in 2025. Key Publications: mCLIP (ACL 2023), ImPart (ACL 2025), MiLoRA (NAACL 2025) Dr. Chen actively seeks motivated PostDoc, PhD, and Master's students. He serves as an Area Chair for ACL and EMNLP and contributes to advancing AI through open-source projects like mCLIP on GitHub.

Aude OlivaView profile

Researcher

• Computer Vision
• Deep Learning
• Generative Models
• +5 more

Aude Oliva serves as MIT director of the MIT-IBM Watson AI Lab and director of strategic industry engagement at the MIT Schwarzman College of Computing. As a Senior Research Scientist at MIT CSAIL, she leads the Computational Perception and Cognition group, driving interdisciplinary research at the intersection of human intelligence and artificial systems. Her roles position her at the forefront of translating academic AI research into real-world applications through major industry partnerships. Dr. Oliva earned her MS and PhD in cognitive science from Institut National Polytechnique de Grenoble, France, establishing her foundation in human perception and computational modeling. Her research integrates computer vision, deep learning, and cognitive neuroscience to understand visual information processing in both biological and artificial systems. She develops computational models that mimic human visual recognition while creating AI systems capable of compositional reasoning and efficient video understanding. Current work emphasizes neuroscience-inspired architectures, resource-efficient deep learning, and multimodal representation learning, with applications spanning healthcare, robotics, and human-computer interaction. Her cross-disciplinary approach uniquely bridges theoretical neuroscience with practical AI development. Analysis of recent publications reveals a clear trajectory toward tighter integration of neuroscience and AI, particularly through brain imaging datasets like BOLD Moments. Her group consistently advances efficient deep learning techniques (Trans-LoRA, VA-RED²) while exploring fundamental questions in visual cognition through projects like the Algonauts Challenge. The work demonstrates increasing industry relevance with strong representation in NeurIPS and Nature Communications. Her major recognitions include: NSF Career Award in computational neuroscience Guggenheim fellowship in computer science Vannevar Bush Faculty Fellowship in cognitive neuroscience As director of the $240M MIT-IBM Watson AI Lab, Dr. Oliva oversees substantial research funding while advising graduate students through MIT's EECS department. Her lab benefits from unique industry-academic synergy, with students gaining access to IBM resources and real-world deployment challenges. The collaborative environment fosters innovation in efficient AI systems with tangible societal impact. The Computational Perception and Cognition group operates as a dynamic hub where computer scientists, neuroscientists, and cognitive scientists collaborate on fundamental questions of intelligence. Current projects focus on making AI systems more human-like in visual reasoning while ensuring computational efficiency for real-world deployment, leveraging the unique resources of the MIT-IBM partnership.

Sanaul HoqueView profile

Senior Lecturer

• Computer Vision
• Biometrics
• Security Systems
• +6 more

Dr. Sanaul Hoque is a Senior Lecturer in Secure Systems Engineering at the University of Kent's School of Engineering and Digital Arts, where he also serves as Course Lead for Electronic and Computer Engineering. His research focuses on biometric security systems, computer vision, and pattern recognition applications with over 79 publications documented in the Kent Academic Repository. Dr. Hoque's primary research interests include: Computer Vision and Pattern Recognition Biometrics and Security Systems Document Analysis and Modeling Encryption and Secure Systems Multi-Expert Fusion Techniques EEG-based Biometric Recognition His extensive publication record spanning over two decades demonstrates expertise in developing innovative biometric security solutions. Recent work focuses on neural network applications for security systems, explainable presentation attack detection, and EEG-based biometric recognition. His research shows a consistent trajectory toward more sophisticated and explainable biometric security systems that address real-world challenges in authentication and identification, with his most recent publication appearing in 2025. Dr. Hoque has maintained productive research collaborations with colleagues including Farzin Deravi, Konstantinos Sirlantzis, and Gareth Howells, resulting in numerous co-authored publications across various domains of biometric security. His work has appeared in reputable journals including Sensors, IEEE Access, and Pattern Analysis and Applications. As Course Lead for Electronic and Computer Engineering, Dr. Hoque plays a significant role in curriculum development and student mentorship within the engineering program at Kent. His expertise bridges theoretical computer science with practical security applications, particularly in biometric systems and secure document modeling.

Dimitrios SoudrisView profile

Professor

• Computer Engineering
• Embedded Systems
• Reconfigurable Architectures
• +5 more

Dimitrios Soudris is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), leading the Microprocessor and Digital Systems Lab (MicroLab). Previously, he served as Lecturer, Assistant, and Associate Professor at Democritus University of Thrace from 1995 to 2008. Diploma in Electrical Engineering (1987), University of Patras PhD in Electrical Engineering (1992), University of Patras His research focuses on Embedded Systems , Reconfigurable Architectures (FPGAs) , Hardware Accelerators for data centers/space/cloud, Edge Computing , and Low Power VLSI Design . Recent publications highlight trends in: AI/ML acceleration for edge and space applications Secure FPGA architectures for 6G networks Energy-efficient heterogeneous memory systems Approximate computing techniques Transformer optimization for low-power contexts Scientific Awards: INTEL and IBM awards (project LPGD #25256) HiPEAC, DAC, and ISCA awards (2010–2024) XILINX Open Hardware Design Contest (2017, 2019, 2021) He has coordinated >70 R&D projects funded by the European Commission, ENIAC-JU, ESA, and industry partners. As Associate Editor of ACM TODAES and conference chair (PATMOS, VLSI-SOC), he contributes to academic leadership. His lab, MicroLab, specializes in hardware-software co-design for emerging computing paradigms.

Swarnendu BiswasView profile

Assistant Professor

• Programming Languages
• Compilers
• Runtime Systems
• +7 more

Swarnendu Biswas is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He teaches courses including Programming for Performance (CS 610), Analysis of Concurrent Programs (CS 636), and Compiler Design (CS 335), demonstrating his expertise across multiple areas of computer systems. His research interests center on Programming Languages, Compilers, Runtime Systems, and Parallel Software Systems. He leads the PROSPAR (Programming Languages and PARallel Systems) research group, which focuses on developing techniques to build efficient and correct parallel software through program analysis, compiler optimizations, and runtime systems. His recent publications reveal a strong trend in addressing fundamental challenges in parallel computing, with work spanning cache coherence, false sharing detection, data race analysis for GPUs, verification of neural networks, and thermal-aware management of heterogeneous systems. His research bridges theory and practice with significant contributions to both hardware and software aspects of parallel systems. His scientific achievements have been recognized through multiple prestigious awards: Google India Research Award 2021 Google Explore CSR 2022 Research Grant from Intel Corporation SERB Start-up Research Grant 2019 Google Cloud Platform Research Credits (2019, 2020) IITK Initiation Grant 2019 As an advisor, he mentors several PhD and MTech students working on cutting-edge research in parallel systems. His PROSPAR group has secured significant funding from industry and government sources, supporting innovative research in programming languages and parallel systems. The group actively collaborates with industry partners including Google and Intel, addressing real-world challenges in parallel computing. He leads the PROSPAR research group at IIT Kanpur, which brings together faculty, PhD students, and MTech researchers to tackle challenging problems at the intersection of programming languages, compilers, and parallel systems. The group maintains strong industry connections and focuses on practical solutions that can be deployed in real systems.

Rickard KarlssonView profile

Lecturer

• Swedish as a second language
• Learner language assessment
• Language development in adults
• +4 more

Dr. Rickard Karlsson works as a Lecturer at Linköping University's Department for Swedish as a Second Language, Rhetoric and Language Support (SAROS) under the Department of Culture and Society (IKOS). His teaching focuses on Swedish language didactics, grammar, and assessment of learner languages, with supervision across academic levels. PhD in Languages and Cultures of Europe Upper Secondary School Teacher in Swedish as a Second Language Research spans empirical analysis of adult language acquisition , historical linguistics , and multilingualism ideologies . Google Scholar publications reveal interdisciplinary contributions to particle filter algorithms and automotive sensor systems from 2001-2025. Notable collaborations include Fredrik Gustafsson and Per-Johan Nordlund. Recent publications (2025-2016) merge automotive engineering and historical Linguistics, covering tire diagnostics, cultural exchange patterns, and vibration-based navigation. This dual expertise reflects his transition from technical research to language education, maintaining academic connections across disciplines.