Find Academic Supervisors

• Energy-efficient circuits and systems
• Low-power wireless sensors
• Multimedia terminals
• +3 more

Anantha P. Chandrakasan is MIT's Provost and Vannevar Bush Professor of Electrical Engineering and Computer Science. He leads strategic initiatives such as the MIT Climate and Sustainability Consortium, MIT AI Hardware Program, and MIT-IBM Watson AI Lab. As Chief Innovation and Strategy Officer, he oversees MIT HEALS, MGAIC, and MITHIC. Previously, he served as MIT School of Engineering Dean (2011-2025) and director of MIT Microsystems Technology Laboratories (2006-2011). He earned all degrees (B.S., M.S., Ph.D.) in EECS from UC Berkeley (1989-1994). His research focuses on energy-efficient circuits, low-power wireless sensors, and emerging technologies. Key projects include implantable medical devices, secure AI hardware, and THz communication systems. He pioneered the Schwarzman College of Computing, reshaping MIT’s academic structure. Recipient of the 2022 IEEE Mildred Dresselhaus Medal, he holds leadership roles in multiple MIT-industry partnerships. Notable collaborations include Ericsson (5G networks), Takeda (healthcare), and Accenture (industry-technology convergence). His academic advising includes fostering interdisciplinary programs like the MIT Quest for Intelligence and postdoctoral fellowships. He champions diversity through initiatives like the Faculty Gender Equity Committee and Daniel J. Riccio Graduate Engineering Leadership Program. Labs/Teams: Leads MIT's Office of Innovation & Strategy, oversees Microsystems Technology Laboratories, and co-chairs the MIT-GE Vernova Energy & Climate Alliance. Key hardware projects include conformable ultrasound patches and battery-free IoT devices.

Maarten van ReeuwijkView profile

Professor

• Urban Fluid Mechanics
• Atmospheric Sciences
• Turbulent Flows
• +10 more

Maarten van Reeuwijk is a Professor of Urban Fluid Mechanics at the Department of Civil and Environmental Engineering, Faculty of Engineering at Imperial College London. He serves as Director of Research and teaches at both undergraduate and postgraduate levels. His research focuses on transport processes in urban environments, atmospheric convection, and turbulent flows, with applications to air quality, urban microclimate, and sustainable infrastructure. He leads a multidisciplinary research group involving Aeronautical, Civil, and Mechanical Engineering experts. Education: MSc in Civil and Environmental Engineering (Fluid Mechanics), Delft University of Technology (1995-2002) PhD in Applied Physics (Turbulent Thermal Convection), Delft University of Technology (2002-2007) Research Interests: Urban fluid mechanics, turbulence dynamics, green infrastructure impacts, microplastics behavior, cloud physics, and large-eddy simulation (LES). His work integrates computational modeling (e.g., uDALES software) with experimental data to address environmental challenges like urban heat islands and pollution dispersion. Awards & Recognition: None explicitly stated in the text. Advising & Grants: Supervises PhD/postdoc researchers in diverse engineering and physics backgrounds. Active in securing grants for urban sustainability projects and computational fluid dynamics research. Labs/Teams: Heads the Urban Fluid Mechanics group and collaborates with Imperial's Grantham Institute and Artificial Intelligence Network. Develops the uDALES LES model for urban flow simulations.

• Quantum Computing
• Spin Qubits
• Semiconductor Nanostructures
• +4 more

Dr. Frederico Martins is a Senior Research Fellow and solid-state physicist specializing in quantum computing with spin qubits. He currently leads the Spin Qubit team at QuantrolOx, a University of Oxford spinout developing machine-learning-based software for quantum processor automation. Previously, he led the Quantum Information team at Hitachi Cambridge Laboratory (University of Cambridge). His work bridges quantum hardware and intelligent software to address scalability and reliability in quantum architectures. Research interests include spin qubit control in semiconductor nanostructures, machine learning integration for device calibration, and overcoming challenges in multi-dot systems such as charge sensing and Pauli spin blockade detection. He focuses on high-fidelity spin manipulation, readout, and scaling to enable robust quantum computing platforms. His articles demonstrate expertise in silicon spin qubits, quantum dot characterization, and noise mitigation. Collaborations with industry and academia aim to advance scalable quantum technologies. Current projects emphasize automating quantum device control through AI-driven algorithms.

Shan JayasingheView profile

Lecturer

• Business Analytics
• Human Resource Management
• Agile Project Management
• +5 more

Shan Jayasinghe is a Lecturer in Business Management at Solent University, UK, specializing in areas such as HRIS adoption, business analytics, agile methodologies, and generative AI applications in education. His research spans interdisciplinary topics including IT adoption in hybrid work environments, customer engagement strategies, and organizational behavior in post-pandemic contexts. His work frequently explores Sri Lankan industries, focusing on software, apparel, construction, and IT sectors. Key themes include the integration of technologies like AI, BIM tools, and AR in driving operational efficiency and customer satisfaction. Jayasinghe has collaborated on multiple peer-reviewed articles and conference proceedings, with recent outputs emphasizing agile project management, CI/CD pipelines, and the application of generative AI in education and corporate training. His publications often employ frameworks like DIT, TAM, and UTAUT to analyze technology adoption. He has no listed awards but maintains active research collaborations, evidenced by contributions to IEEE conferences and journals like Education Sciences and Journal of Contemporary Management Issues .

Vittorio CurriView profile

Professor

• Optical Communications
• Optical Networks
• Multi-band Optical Networks
• +12 more

Vittorio Curri is a Full Professor in Optical Communications and Networking at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino , Italy. He is a founding member of the OptCom Group and PhotonLab and leads the PLANET research team. His work spans optical network modeling, AI-assisted control, open-source software (GNPy), and environmental sensing via optical fiber. He has led numerous EU and industry-funded projects and is a key figure in open optical networking. Research Interests: Multi-band optical transmission in single-mode fibers Physical layer aware networking Machine learning and AI for optical network optimization Environmental sensing using optical network telemetry Open and disaggregated optical networks Digital twin development (GNPy project) WDM and coherent transmission modeling The recent publications reflect a strong trend toward integrating AI and machine learning with digital twin technologies for real-time network control, anomaly detection, and performance optimization. There is a clear focus on wideband, multi-band, and converged metro-access networks , with modeling efforts extending to filtering effects, polarization, and nonlinear impairments. The research is highly applied, with strong industry collaboration and open-source contributions. Scientific Awards: Concorso "Galileo Feraris" (2002) JLT Best Paper Award (2014, 2015) FFABR 2017 Research Grant Advising and Grants: Prof. Curri has supervised 25 PhD students and numerous postdocs. He has served as Principal Investigator on major projects including WON (EU H2020) , ALLEGRO , NESTOR , RESTART , SENSEI , and SCIPIO . He leads the GNPy open-source project under TIP and has extensive collaborations with Synopsys, Open Fiber, INFINERA, and CESNET. His research is funded by EU programs (Horizon Europe, H2020), PNRR, and multiple industrial contracts. Labs and Teams: He leads the PLANET (Physical Layer Aware NETworking) team, a subgroup of the OptCom Group at PoliTo. The team focuses on simulation, modeling, and AI-driven control of next-generation optical networks. They collaborate closely with the LINKS Foundation and international partners including Soochow University and CESNET .

• Signal processing for communications
• MIMO communications
• Wireless, ad hoc and sensor networks
• +11 more

Nuno Manuel Branco Souto is an Associate Professor (with Habilitation) at the Department of Information Science and Technology, School of Technologies and Architecture, ISCTE - University Institute of Lisbon. He also holds leadership roles as Deputy Director of both the School of Technologies and Architecture and the Institute of Telecommunications - IUL, where he is an Integrated Researcher in the Radio Systems Group. Aggregation in Information Sciences and Technologies, ISCTE-University Institute of Lisbon, 2021 PhD in Electrical and Computer Engineering, Higher Technical Institute - UTL, 2006 Bachelor's degree in Aerospace Engineering, Higher Technical Institute - UTL, 2000 His research focuses on signal processing for communications , MIMO systems , channel estimation , reconfigurable intelligent surfaces (RIS) , and optimization in wireless communications . He explores advanced techniques in hybrid precoding , index modulation , and energy-efficient 5G/6G network design , contributing significantly to next-generation wireless technologies. His recent publications (2020–2024) reveal a strong trend toward reconfigurable intelligent surfaces (RIS) , millimeter-wave and THz communications , molecular communication with deep learning , and anti-UAV systems using SDR-based jamming and spoofing . These works appear in high-impact journals such as IEEE Transactions, Sensors, and Applied Sciences, reflecting his leadership in cutting-edge wireless communication research. Dr. Souto has no scientific awards explicitly listed in the provided text. He has been actively involved in teaching, research supervision, and academic leadership. His collaborations span multiple institutions and include prominent researchers such as Pavia, Velez, Correia, Sebastião, and Dinis. He contributes to both theoretical advancements and practical implementations in wireless systems, including software-defined radio (SDR) applications for UAV security and scalable video broadcasting in cellular networks. Dr. Souto is affiliated with the Radio Systems Group at the Institute of Telecommunications - IUL, where his team conducts research on next-generation wireless networks, signal processing algorithms, and communication system optimization.

Jonathan P. MathewsView profile

Professor

• Coal Science
• Carbon Materials
• Energy Engineering
• +3 more

Jonathan P. Mathews is a Professor of Energy and Mineral Engineering at The Pennsylvania State University, specializing in coal science and technology. He holds the title of ACS Fellow and leads research on coal structure, atomistic modeling, and carbon materials. His work focuses on the relationship between coal structure and behavior, including applications in coalbed methane, CO₂ sequestration, and carbon product development. He teaches courses on energy science, environmental energy systems, and oversees the Penn State Coal Research and Sample Banks. Education: Ph.D. in Fuel Science from Penn State (1998). Research interests include molecular modeling of coal/char/soot, advanced analytical techniques, and collaborations with institutions globally. He has authored over 95 journal articles and developed software tools like Fringe3D for structural analysis. His research highlights include: atomistic representations of coal for combustion/gasification studies, microwave applications for coal processing, and structural characterization of coal macerals. Awards include recognition as an ACS Fellow. Advises students in energy and mineral engineering, focusing on coal science, and collaborates with national labs and international partners. Labs/Teams: Leads the Penn State Coal Research Group, advancing computational and experimental methods in coal science. His work bridges geoscience, materials science, and energy engineering, addressing sustainable energy challenges through interdisciplinary approaches.

Lim Mei KuanView profile

Senior Lecturer

• Computer Vision
• Machine Learning
• Smart Manufacturing
• +4 more

Lim Mei Kuan is a Senior Lecturer at the School of Information Technology, Monash University Malaysia. She holds a PhD in Computer Vision from Universiti Malaya and has conducted post-doctoral research in Artificial Intelligence. Her career includes roles as a researcher at MIMOS Berhad (2004-2007) and visiting researcher at Kingston University (2012-2013). Education: BSc (First Class Honours) in Computer Science, Universiti Malaysia Sarawak (2007) PhD in Computer Vision, Universiti Malaya (2015) Research Interests: Her work spans computer vision, machine learning, and their applications in smart manufacturing and digital health. Current projects include social media analytics for mental health monitoring, anomaly detection in manufacturing, and collaboration on clinical teletherapy systems. She actively explores swarm intelligence and deepfake detection technologies. Grants & Collaborations: DELTA: Inclusive Teletherapy (2022-2024) ADSM: Anomaly Detection for Smart Manufacturing (2022-2023) Sense-ED: Eating Disorder Relapse Detection (2021-2022) Awards: ITEX 2022 Silver Award for Self-Mind mental health app Labs & Teams: Active contributor to interdisciplinary projects with industry partners, focusing on AI-driven solutions for healthcare and manufacturing sectors.

Christopher RügerView profile

Researcher

• Analytical Chemistry
• Mass Spectrometry
• High-Resolution Mass Spectrometry
• +11 more

Dr. Christopher Rüger is a research group leader and habilitation candidate at the Chair of Analytical Chemistry, University of Rostock, within the Interdisciplinary Faculty Life Light & Matter. He leads the 'High-Resolution Mass Spectrometry' group and has been active in research since 2015, following his B.Sc. and M.Sc. in Chemistry from the same university. He completed his doctorate in 2018 and held a postdoctoral position at the University of Rouen, France, before returning to Rostock in 2019. Research Interests: His work focuses on developing and applying advanced mass spectrometry techniques, particularly high-resolution and FT-ICR MS, coupled with thermal analysis and novel ionization methods (photo-, laser-, chemical ionization). He investigates complex materials including petroleum, bitumen, aerosols, polymers, and combustion particulates, with applications in energy, environmental science, and materials chemistry. The trend in his recent publications shows a strong emphasis on molecular-level characterization of complex organic mixtures using hyphenated analytical techniques. His research bridges analytical chemistry with environmental, materials, and astrochemistry, particularly through collaborations on ship emissions, wildfire particulates, Titan’s atmospheric analogs, and polymer degradation. He frequently employs ion mobility spectrometry and innovative data processing to enhance structural elucidation. Scientific Recognition: No formal scientific awards or fellowships are listed in the provided texts. Advising and Grants: While no formal students are listed, Dr. Rüger leads a research group and collaborates widely. He is involved in several funded projects, including the European Network of FT-ICR MS Centers, the AerOrbi Eurostars project on aerosol photoionization, SAARUS (scrubber emissions), TBI (thermolysis reactor development), and a DFG-RFBR German-Russian collaboration on wildfire particulates. These projects reflect his interdisciplinary approach and international collaborations, particularly with the iC2MC Lab in France. Laboratories and Teams: He heads the 'High-Resolution Mass Spectrometry' research group at the University of Rostock and maintains a close collaboration with the iC2MC (International Complex Matrices Molecular Characterization) Lab at CNRS, France. His work is conducted within the Department of Analytical Chemistry, Institute of Chemistry, under the interdisciplinary umbrella of 'Life – Light – Matter,' facilitating cross-domain research.

Markus BorgView profile

Lecturer

• Artificial Intelligence
• Software Engineering
• Empirical Software Engineering
• +11 more

Dr. Markus Borg is a Senior Researcher and Adjunct Lecturer at Lund University, Sweden, specializing in the intersection of software engineering and applied artificial intelligence. He is a Principal Researcher at CodeScene and contributes to editorial boards for Empirical Software Engineering and IEEE Software . His work bridges academic research with industrial practice through collaborations with Ericsson and contributions to open-source tools like SMIRK. Markus's research focuses on empirical software engineering , technical debt , safety engineering , and requirements engineering for AI-integrated systems. He explores two key directions: AI4SE (applying machine learning to software engineering challenges like defect management and technical debt remediation) and SE4AI (ensuring quality assurance for ML components in safety-critical domains such as automotive systems). His work aligns with regulatory frameworks like the EU AI Act and industry standards for automotive safety. 2025 Contributions : Gamify Track: Two papers on gamification in software maintainability ICSE Journal-First: Longitudinal study on automated bug assignment at Ericsson IDE Track: Trust calibration in AI-assisted refactoring TechDebt Technical Papers: ACE tool for LLM-based technical debt remediation 2024 Contributions : PROFES: AI Act compliance in requirements engineering MO2RE: Code quality specifications TechDebt: Maintainable code ROI analysis Programming with AI: CodeScene platform demonstration 2023 Contributions : CAIN: ML testing in automotive perception systems EASE: Code ownership and defect resolution Mutation Track: Safety-critical mutation testing validation His recent publications emphasize automated defect management , LLM-driven refactoring , and safety validation for ML components , particularly in automotive contexts. Tools like SMIRK and ACE demonstrate practical applications of his research. Markus actively participates in program committees for ICSE, TechDebt, and ICSME, with a focus on bridging academic research with industrial AI/SE challenges.

• Computer Systems
• Cybersecurity
• Systems Security
• +12 more

Jingjie Li is a Lecturer (Assistant Professor) in the School of Informatics at the University of Edinburgh, where he conducts interdisciplinary research at the intersection of computer systems, cybersecurity, and human-computer interaction. He is a member of the Institute for Computing Systems Architecture and the School of Informatics Ethics Committee. Ph.D. in Computer Engineering, University of Wisconsin-Madison (2017–2023) B.Eng. (R&D) with First-Class Honours, Australian National University (2015–2017) B.Sc., Beijing Institute of Technology (2013–2015) His research focuses on user-centric security and privacy , measuring human behavior in digital systems , and efficient human-machine interfaces . He investigates risks in emerging technologies such as smart homes, AR/VR, and AI systems, aiming to make them safer and more human-centric. His work combines technical innovation with behavioral insights to design practical privacy controls, measure digital risks, and build efficient computing platforms. The recent publications highlight a strong trend in privacy transparency , AI explainability , smart home and AR/VR security , and hardware-software co-design . His work frequently appears in top-tier venues including IEEE S&P, USENIX Security, ACM CHI, and ISCA, reflecting a consistent focus on both technical depth and human factors. Notable scientific awards include: ACM CHI Best Paper Award (2019) Facebook Trustworthy Products in AR, VR, and Smart Devices Award (2021) CPS Rising Star, NSF (2022) Generative AI Laboratory Seedcorn Award, University of Edinburgh (2024) Qualcomm Innovation Fellowship Finalist (2019, 2021) Jingjie Li actively supervises PhD students including Jiuming Jiang and Karen Jiamin Zheng, and co-supervises Lawrence Piao and Temima Hrle. He has received research support through fellowships such as the UW–Madison Chancellor’s Opportunity Fellowship and has collaborated globally with institutions including Max Planck Institute, Visa Research, and CSIRO. He serves on the program committees of major conferences like ACM CCS, USENIX Security, and ACM CHI. He leads a dynamic research team focused on systems security and human-centered computing, hosting undergraduate researchers and mentoring students through projects in privacy, AI transparency, and hardware security. His lab fosters interdisciplinary collaboration and real-world impact through community engagement, such as the 'Hack Your Age' workshop with intergenerational participants.

• Reinforcement Learning
• Safe AI
• Artificial Intelligence
• +7 more

Javier García is a faculty member at the University Carlos III of Madrid, affiliated with the Department of Computer Science within the School of Engineering. His research lies at the intersection of artificial intelligence, reinforcement learning, and robotics, with a strong emphasis on safety, transferability, and real-world applications. His research interests include: Safe and robust reinforcement learning Transfer and multi-task learning in AI Adversarial attacks and defenses in deep RL Automated planning and decision-making under uncertainty Applications in social assistive robotics and financial systems The recent trend in his publications shows a focus on formalizing similarity between decision-making tasks, improving robustness against corrupted rewards and adversarial policies, and applying AI techniques to domains such as geriatric care and automated market making. His work often involves collaboration with Fernando Fernández and other researchers in Spain. He has contributed significantly to frameworks like CLARC for comprehensive geriatric assessment using robots, and has developed tools such as TIMIPLAN for transportation tasks. His research bridges theoretical AI with practical deployment in cognitive robotics and business simulators. Notable scientific contributions include a comprehensive survey on safe reinforcement learning (JMLR 2015), foundational work on policy reuse, and the development of taxonomies for MDP similarity. He has secured research funding through collaborative projects, though specific grants are not detailed in the provided text. He has advised several students and junior researchers, evident from co-authored publications, though specific names are not listed. He is part of research teams working on lifelong learning technologies and cognitive robot systems.

Mahsa YarmohammadiView profile

Researcher

• Natural Language Processing
• Deep Learning
• Automatic Speech Recognition
• +4 more

Mahsa Yarmohammadi is an Assistant Research Scientist at the Center for Language and Speech Processing (CLSP), where she leads research in cross-lingual language and speech applications. Her work focuses on deep learning techniques for multilingual data representation, automatic speech recognition (ASR), and speech translation. She develops novel algorithms for ASR decoders and explores interfaces between ASR and machine translation systems. Education: PhD in Computer Science and Engineering, Oregon Health & Science University (2016) Master’s in Computer (Software) Engineering, Shahid Beheshti University, Iran (2007) Undergraduate degree in Computer (Software) Engineering, Amirkabir University of Technology (Tehran Polytechnic) (2004) Research Interests: Her expertise spans Natural Language Processing (syntactic/semantic parsing, information extraction), multilingual systems, deep learning, and ASR. She specializes in resource transfer across languages, neural lattice embeddings, and end-to-end system integration for speech and language technologies. Publication Trends: Recent articles (2019–2025) emphasize multilingual challenges: cross-lingual retrieval, contextual ASR, coreference resolution in dialogue, and zero-shot information extraction. Her work frequently addresses low-resource settings, neural algorithm design, and real-world dataset creation (e.g., earnings call analysis, COVID-19 QA pairs). Grants & Teams: As a prime researcher in multi-site programs, she collaborates on large-scale multilingual projects. She was mentored by Benjamin Van Durme (Johns Hopkins CS) and Sanjeev Khudanpur (Johns Hopkins ECE) during her CLSP post-doctoral fellowship (2021).

• Mechanical Engineering
• Engineering Design Theory and Methodology
• Multiobjective Optimization
• +6 more

Christopher A. Mattson is a Professor of Mechanical Engineering at Brigham Young University (BYU) and director of the Design Exploration Research Group. With a PhD from Rensselaer Polytechnic Institute (2003), he has received prestigious awards including the Presidential Early Career Award (PECASE), NSF CAREER Award, Fulbright Scholarship, and ASME Fellowship. Education: PhD, Mechanical Engineering, Rensselaer Polytechnic Institute (2003) MS, Mechanical Engineering, BYU (2001) BS, Mechanical Engineering, BYU (1999) Research Interests: Focus on engineering design theory, multiobjective optimization, and social impact modeling for global development. Applications span poverty alleviation, sustainable design, and modular product platforms. He develops formal methodologies integrating computational optimization with social and environmental factors during conceptual design stages. Teaching Interests: Specializes in team-based engineering design, teaching courses like Advanced Product Development and Global Product Development. His pedagogy emphasizes interdisciplinary collaboration, industry-sponsored projects, and global engagement through travel and cross-cultural partnerships. Scientific Awards: ASME Fellow (2020–Present) NSF CAREER Award (2010–2014) Presidential Early Career Award (PECASE) (2012) Ben C. Sparks Medal (ASME) (2015) Fulbright Scholar (2014–2015) Professional Service: Holds leadership roles in ASME committees, serves as Associate Editor for Journal of Mechanical Design , and contributes to multidisciplinary design optimization societies. His work bridges academia, industry, and international development through sponsored projects and research collaborations.

• Software Engineering
• Empirical software engineering
• Global software engineering
• +2 more

Casper Lassenius serves as an Associate Professor in the Department of Computer Science at Aalto University, Finland, where he is affiliated with the Software and Service Engineering (SSE) research group. His academic role focuses on advancing empirical methodologies within software engineering through rigorous research and industry collaboration. Professor Lassenius's research centers on Empirical Software Engineering, with specialized expertise in Agile and Lean software development methodologies. His work critically examines large-scale agile transformations, global software engineering challenges, and the delicate balance between organizational control and team autonomy. He investigates practical implementations of scaling frameworks like SAFe across diverse sectors including finance and public administration, addressing real-world adoption barriers and optimization strategies. Analysis of his 2020-2024 publications reveals dominant research trajectories in agile scaling frameworks, backsourcing of IT services, and organizational dynamics in large-scale development. His empirical studies consistently employ systematic literature reviews and case-based approaches to identify challenges like delay archetypes and autonomy-control tensions, providing actionable insights for enterprise agile adoption. Lassenius is an active contributor to the Software and Service Engineering (SSE) research group, which drives innovation in software processes, service design, and empirical validation of development practices. This collaborative environment enables his research to bridge theoretical rigor with industry applicability, particularly in complex organizational contexts.