Find Academic Supervisors

Bishal LamichhaneView profile

Academic

• Bio-behavioral modeling
• Wearables and Mobile Sensing
• Mental health applications
• +4 more

Bishal Lamichhane is an Assistant Research Professor at Rice University's Electrical and Computer Engineering department, affiliated with the Digital Health Lab. He focuses on bio-behavioral machine learning solutions for mental health diagnosis and monitoring, leveraging speech, wearables, mobile sensing, and imaging. With industrial experience at Philips Research and Samsung, he holds a PhD from Rice, MS from TU/e (cum laude), and BTech from SVNIT. Education: PhD in Electrical and Computer Engineering, Rice University MS in Electrical Engineering (Signal Processing), TU/e B.Tech in Electronics and Communication, SVNIT Research Interests: Bio-behavioral modeling, wearables, mental health applications, and machine learning. His work bridges clinical and industrial innovations, addressing challenges like psychotic relapse prediction and depression severity assessment using multimodal data. Articles Trends: Recent work emphasizes AI-driven mental health tools, including impulsivity modeling, depression monitoring via mobile sensing, and leveraging ChatGPT for diagnostics. He explores both clinical and cybersecurity applications, such as wireless network analysis. Awards: Includes Rice Future Faculty Fellow, ICC Best Paper Award, and multiple academic scholarships. Grants & Roles: Secured NSF ERC seed funds, serves as adjunct professor at Baylor College of Medicine, and guest editor for PLOS Digital Health. He leads the 'Machine Learning Algorithms for Schizophrenia Detection' research topic. Labs: Digital Health Lab (Rice), Scalable Health Labs, and collaborations with Harris Health/Baylor College of Medicine.

Lori LevinView profile

Research Professor

• Computational Linguistics
• Low-Resource Languages
• Machine Translation
• +6 more

Lori Levin is a Research Professor at Carnegie Mellon University's Language Technologies Institute (LTI) within the School of Computer Science. Her research focuses on computational linguistics, particularly documenting endangered languages, linguistic typology, and developing multilingual NLP systems for low-resource scenarios. She co-founded and co-chairs the North American Computational Linguistics Olympiad (NACLO) and leads the LLab, which explores human language and computational modeling. Levin teaches advanced linguistics courses including Grammar Formalisms and ConLanging. Her work emphasizes hybrid approaches combining human-engineered linguistic knowledge with machine learning to address data scarcity challenges. Notable projects include the Spoken Mapudungun corpus (Chilean minority language) and the Linguistic-Core Approach for low-resource language analysis. Research interests span corpus annotation frameworks, interlinear glossing systems, and the application of typological universals to NLP. She has pioneered methodologies for rapid NLP system development in linguistically under-resourced contexts. Levin collaborates internationally on initiatives like the MILE Corpus for Less Commonly Taught Languages and NESPOLE! multilingual speech communication systems.

Alon LavieView profile

Academic

• Machine Translation
• Natural Language Processing
• Computational Linguistics
• +9 more

Dr. Alon Lavie is a Consulting Professor at the Language Technologies Institute (LTI) within Carnegie Mellon University (CMU), where he has been a faculty member since 1996. He previously served as a Research Professor at CMU from 1996 to 2015. Lavie is also currently the VP of AI Research at Phrase, leading AI R&D teams across Pittsburgh, Prague, and Edinburgh. His work bridges academic research and industrial application in machine translation (MT) and natural language processing (NLP). Academic Affiliations: CMU (Consulting Professor, 1996–present), Unbabel (VP of Language Technologies, 2015–2019), Amazon (Senior Manager, 2015–2019) Industry Roles: Co-founded Safaba Translation Solutions (2009–2015), acquired by Amazon Research Interests: Lavie’s research focuses on Machine Translation and Natural Language Processing , particularly syntax-driven and hybrid approaches to MT, human feedback integration, and evaluation metrics. His projects include: AVENUE/LETRAS: Developing MT systems for minority languages (Mapudungun, Quechua, Inupiaq). METEOR: Leading the decade-long development of this highly correlated MT evaluation metric. COMET: Directed development of a neural MT evaluation metric at Unbabel. MEMT: Pioneering multi-engine MT combination frameworks. GRASP: Robust parsing of grammatical relations in spoken language corpora. Scientific Awards: Recipient of the 2021 Makoto Nagao IAMT Award of Honour for contributions to MT. Former president of the International Association for Machine Translation (2013–2015) and Association for Machine Translation in the Americas (2008–2012). Advising & Leadership: Supervised 23 graduate students, including PhD recipients Ricardo Rei (2024) and Austin Matthews (2019). Served as General Chair for AMTA 2010/2012, MT Summit 2015, and as president of ACL’s SIGParse (2008–2013).

• Applied Artificial Intelligence
• Robotics
• Unmanned Aerial Vehicles (UAVs)
• +5 more

Eleni Mangina is a Full Professor at the School of Computer Science, University College Dublin (UCD), and Vice Principal (International) for the College of Science. Her research focuses on applied artificial intelligence (AI), robotics, unmanned aerial vehicles (UAVs), and extended reality (XR) technologies with interdisciplinary applications in energy systems and education. She holds a PhD from the University of Strathclyde (UK), an MSc in Artificial Intelligence from the University of Edinburgh, and an MSc in Agricultural Science from the Agricultural University of Athens. Education : PhD, University of Strathclyde (UK), 2001 MSc in Artificial Intelligence, University of Edinburgh (UK) MSc in Agricultural Science, Agricultural University of Athens (Greece) HDip in University Teaching & Learning, UCD Research Interests : AI-driven optimization for energy and materials Xr applications in healthcare and education Citizen science and open data practices Robotics in early childhood education Smart city technologies Awards & Honors : 2022 CEN/CENELEC Standards Innovation Award 2021 Athena SWAN Bronze Award (School of Computer Science) 2020 UCD President's Teaching Award 2022 StandICT.eu Fellowship Grants & Projects : Coordinator of EU H2020 projects: ARETE, AHA, and FANTASIA Principal Investigator in SFI Energy Systems Integration Programme Lead on multiple XR and energy-related grants (2017-2025) Labs & Teams : Her lab develops XR solutions for education and energy, collaborating with EU and international partners. Current focuses include ethical XR standards and AI integration with metaverse platforms.

Alessio OrsinoView profile

Research Fellow

• Big Data Analysis
• Distributed Computing
• Edge-Cloud Computing
• +5 more

Alessio Orsino is a Research Fellow at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES) at the University of Calabria. His academic journey includes a Ph.D. in Information and Communication Technologies (ICT) awarded in January 2025, a Master’s in Computer Engineering (Big Data Analytics and Data Science) from 2020, and a Bachelor’s in Computer Engineering from 2018, all from the University of Calabria. He also served as a visiting researcher at the University of Cambridge’s Department of Computer Science and Technology (July–December 2023). His research focuses on big data analysis, distributed computing, machine learning, and edge-cloud integration. He contributes to projects like ASPIDE (Exascale programming models) and INSIDER (cloud-edge IoT deployment optimization), funded by EU Horizon 2020 and Italy’s MIUR. He is a contract professor at DIMES and a member of the Scalable Computing and Cloud Laboratory (SCALab). Orsino’s work bridges theoretical and applied computing, emphasizing efficient machine learning on resource-constrained systems and real-time adaptive models for edge environments. His publications address edge-cloud continuum challenges, adversarial robustness in AI, and scalable big data frameworks. Key grants include Horizon 2020 (ASPIDE) and PRIN 2022 (INSIDER), supporting his interdisciplinary research in hybrid cloud-edge infrastructures and data-intensive applications. He has authored/co-authored a book on big data programming and collaborates internationally through visiting positions and lab engagements.

Isaac O. SorinolaView profile

Professor

• Physiotherapy
• Stroke Rehabilitation
• Chronic Pain Management
• +3 more

Professor Isaac O. Sorinola is a qualified physiotherapist and course leader for the Physiotherapy MSc program at London Metropolitan University. Prior to joining London Met in November 2021, he led the physiotherapy programme at King's College London. His research focuses on neurorehabilitation, chronic pain management, and culturally adapted healthcare interventions in low-literacy populations, with a particular emphasis on stroke recovery, low back pain, and occupational health challenges in developing countries. He has pioneered studies on transcranial stimulation techniques, cross-cultural validation of assessment tools, and community-based self-management programs in rural African settings. His work bridges clinical practice with global health equity, emphasizing culturally relevant approaches to healthcare delivery. Research Interests: His primary areas of investigation include stroke rehabilitation strategies , cultural adaptation of healthcare tools , chronic low back pain epidemiology , and biomechanical risk factors in manual labor populations . He has explored the efficacy of neuromodulation techniques like transcranial direct-current stimulation and has conducted extensive studies on psychosocial factors influencing pain perception and disability assessment in Nigerian Igbo communities. Publications Trends: Over 15 years, his work has evolved from foundational studies on upper limb recovery post-stroke (2005–2015) to contemporary investigations into culturally tailored healthcare solutions for chronic conditions. Recent studies highlight methodological innovations in cross-cultural validation of assessment scales and feasibility of community programs in resource-poor settings. Awards: No scientific awards explicitly mentioned in available texts, though his contributions to global physiotherapy practice are widely recognized in academic circles. Advising & Grants: No formal advisee名单 or grant details provided in the text. His current role as course leader emphasizes educational program development rather than direct doctoral supervision. Labs/Teams: No specific lab affiliations listed, though collaborations with King's College London and Nigerian healthcare institutions are implied through publication themes.

Abdel-Karim Al-TamimiView profile

Senior Lecturer

• Machine Learning
• Natural Language Processing
• Multimedia Networks
• +3 more

Dr. Abdel-Karim Al-Tamimi is a Senior Lecturer in Computer Science and Software Engineering at Sheffield Hallam University (SHU), where he leads the Interactive Data Analytics Group (iDAG) and contributes to the Applied Software Engineering Research Group (ASERG). He previously served as an Associate Professor at Yarmouk University (Jordan), Director of the Entrepreneurship and Innovation Center (EIC), and Department Chair at the Higher Colleges of Technology (UAE). His roles include consultancy in digital transformation via the DIfG program and editorial board memberships for PLOS ONE and Research Reports on Computer Science . Education: MSc and PhD in Computer Engineering, Washington University in St. Louis (2007, 2010) Senior Fellow of the Higher Education Academy (SFHEA) Research Interests: Machine Learning applications in Natural Language Processing (NLP), Multimedia Networks, Computer Security, and IoT. Recent work focuses on conversational agents (e.g., Phyllis chatbot for adolescent health) and cybersecurity education frameworks. Projects include EU-funded initiatives (e.g., H2020, DFG) and interdisciplinary collaborations in AI-driven solutions. Publications: Over 50+ peer-reviewed articles, including recent contributions on optimization algorithms, game-based learning, and threat intelligence models. His work spans journals like Cluster Computing , ACM Transactions on Computing Education , and conferences such as IEEE BigComp. Grants & Awards: Received £10k for the Phyllis chatbot project and £9.8k for Sheffield’s population health initiative. Awards include SFHEA and industry certifications (Huawei HCIA-AI, Cisco CCNA). Advising & Teams: Oversees interdisciplinary projects and labs like the Orange-Yarmouk Innovation Lab. Active in academic entrepreneurship through roles like Co-Founder of Phyllis and mentor at global hackathons.

Fahad DogarView profile

Associate Professor

• Networking
• Distributed Systems
• Cloud Computing
• +5 more

Fahad Dogar is an Associate Professor in the Department of Computer Science at Tufts University's School of Engineering, with a secondary appointment at the Jonathan Tisch College of Civic Life. He leads the NAT (Networking At Tufts) research group and serves as Director of the Master Software Systems Program since 2022. His academic journey includes a Ph.D. from Carnegie Mellon University (2012), undergraduate studies at Lahore University of Management Sciences (LUMS) in Pakistan (2005), and postdoctoral research at Microsoft Research UK. Dogar's research focuses on designing technologies for social impact across multiple computer science domains. His primary interests include networking and distributed systems, with recent emphasis on data center networking, future Internet architectures, and the application of large language models for accessibility. Notably, his work spans cloud-based systems, mobile and wireless technologies, and developing practical solutions for resource-constrained environments in developing regions. Networking and distributed systems architecture Cloud computing infrastructure and optimization Human-Computer Interaction for accessibility Generative AI applications for social impact Technologies for developing regions Augmented reality networking requirements His publication record shows consistent output with 36 publications including two significant papers in 2024 focusing on LLM-powered applications for autistic users and machine learning job scheduling. His research has appeared in top-tier venues including ACM SIGCOMM, Usenix NSDI, and ACM MobiCom. Gold medal from the president of Pakistan for top computer science student LUMS Vice Chancellor Alumni Achievement Award (2021) VMWare Early Career Faculty Fellowship (2019) Facebook/Oculus Faculty Fellowship (2017) Tisch Faculty Fellowship (2017-2018) Dogar has secured substantial research funding including an NSF Core Medium award ($850K) as sole PI for 'Slack-Aware Networking' (2021-2025), Meta/Facebook funding ($150K) for networking support for telepresence applications (2020-2022), and multiple NSF awards totaling over $1 million. His teaching portfolio includes courses on Networks, Computing for Developing Regions, and special topics in Generative AI for Social Impact. During his 2023 sabbatical, he served as Senior Fellow with the Burnes Center for Social Change at Northeastern University, demonstrating his commitment to civic technology applications.

Enzo TartaglioneView profile

Associate Professor

• Deep Learning
• Model Compression
• Pruning
• +6 more

Enzo Tartaglione serves as Associate Professor at Télécom Paris, Institut Polytechnique de Paris, holding a Hi!Paris chair and contributing as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems. His academic journey spans multiple institutions across Europe and the US, reflecting a strong interdisciplinary foundation. Educational milestones include: MS in Electronic Engineering, Politecnico di Torino (2015, cum laude) MS in Electrical and Computer Engineering, University of Illinois at Chicago (2015, magna cum laude) MS in Electronics, Politecnico di Milano (2016, cum laude) PhD in Physics, Politecnico di Torino (2019, cum laude), thesis: 'From Statistical Physics to Algorithms in Deep Neural Systems' His research centers on efficient deep learning , with pioneering work in model compression, neural pruning, and debiasing techniques. He actively develops methods for privacy-aware learning and green AI, targeting real-world deployment constraints in computer vision and medical imaging applications. His approach bridges theoretical physics with practical AI optimization. Recent publications (2024-2025) demonstrate consistent focus on computational efficiency, with 60% of works addressing model compression for vision tasks, 25% on bias mitigation, and emerging contributions in privacy preservation. Key venues include ICCV, CVPR, and IEEE Transactions, reflecting strong industry-academia impact. Scientific recognition includes: Finalist for Multimedia Rising Star Award (2025) He mentors 10 active PhD candidates across compression, debiasing, and on-device learning domains, having previously guided 3 PhD graduates and 17+ Master's researchers. Research funding includes the Hi!Paris GIFFAI project (2025) for frugal AI and ANR's BANERA initiative (2024) on bias-aware architecture search. His group operates within Télécom Paris' joint laboratory, driving the Frugal AI initiative through collaborations with ELLIS Society partners and industry stakeholders focused on sustainable deep learning deployment.

Rehan AkbarView profile

Teaching Professor

• Software Engineering
• Cybersecurity
• Human-Computer Interaction
• +3 more

Dr. Rehan Akbar is an Associate Teaching Professor at Florida International University's Knight Foundation School of Computing and Information Sciences. He has over 22 years of experience in academia and industry, specializing in outcome-based education and curriculum design. He holds a PhD in Information Technology from Universiti Teknologi PETRONAS (2013), and advanced degrees from multiple institutions in Pakistan. His research focuses on cybersecurity, software engineering, human-computer interaction, and big data usability. He has secured multiple grants for ransomware detection, cloud computing optimization, and software project management. His work includes developing frameworks for intrusion detection systems and improving real-time data transmission in IoT networks. Dr. Akbar has led research groups such as 'Software Engineering and Knowledge Management' and 'Software, Systems, and Humans'. He is a Senior Member of IEEE and contributes to peer-reviewed journals/conferences as a reviewer and editor. His teaching spans software project management, database systems, and e-commerce courses. Recent research trends show strong emphasis on ransomware mitigation strategies, deep learning applications in healthcare, and optimizing cloud/IoT systems. His 2024 publications address ransomware taxonomy, early pandemic detection via AI, and cybersecurity insurance frameworks.

• Human Health-focused Machine Learning/Deep Learning
• Computer Vision
• Medical Image Analysis
• +5 more

Dr. Guy C. Hembroff is an Associate Professor in the College of Computing at Michigan Technological University (MTU), serving as Graduate Program Director for the MS in Health Informatics and PhD in Computational Science and Engineering programs. He holds a PhD in Computational Science and Engineering from MTU, alongside an MPA in Public Administration (Northern Michigan University) and a BS in Finance and Economics (MTU). His research focuses on human health applications of machine learning, computer vision, cybersecurity, and medical device innovation, particularly in areas like medical image analysis and mHealth solutions. He leads the Biomedical Data Science (BDS) Lab, a multidisciplinary team addressing pressing healthcare challenges through AI-driven solutions. Education: PhD, Computational Science and Engineering, Michigan Tech MPA, Public Administration, Northern Michigan University BS, Finance and Economics, Michigan Tech Research Interests: Dr. Hembroff’s work spans healthcare AI (deep learning, medical image segmentation, generative AI), cybersecurity for healthcare systems, and biometric development. His lab collaborates with medical institutions to advance disease surveillance, healthcare interoperability, and secure patient data systems. Recent projects include automated quality control of radiographs and blockchain-based patient data auditing. Advising & Grants: Advises four PhD students on topics like medical image segmentation, mental health intervention modeling, and privacy-preserving LLMs. His work integrates clinical, engineering, and cybersecurity expertise to drive practical healthcare solutions. While specific grant details are not listed, his research emphasizes translational applications in global health equity and rural healthcare access. Labs/Teams: The BDS Lab combines clinical informatics, AI, and cybersecurity to innovate in areas such as medical device development and health data security. Current projects include blockchain for secure patient data and AI-driven mHealth tools for underserved regions.

Lars Ailo BongoView profile

Professor

• Operating Systems
• Concurrent Programming
• Data-intensive Computing
• +5 more

Lars Ailo Bongo is a Professor at the Department of Computer Science, UiT The Arctic University of Norway. His research interests span operating systems, concurrent programming, data-intensive computing, and applications of machine learning in healthcare and bioinformatics. He has been actively involved in teaching courses such as Operating Systems, Concurrent Programming, and Algorithms, emphasizing open-access educational materials. Notably, three of his master’s students received awards for their theses: Nina Angelvik (2018), Bjørn Fjukstad (2014), and Martin Ernstsen (2013). He also advises students like Johan Ravn, whose master’s project led to a startup. Bongo’s work includes developing scalable bioinformatics pipelines (e.g., NeLS infrastructure), optimizing genomics workflows, and advancing tools for medical image analysis. He pioneered open-access course repositories on GitHub and contributed to visualization frameworks like GeneNet VR. His research bridges computer science with healthcare, focusing on tumor infiltration lymphocyte analysis, AI-driven diagnostics, and ethical AI applications in mental health. He has held roles as a postdoc at Princeton and TA at the University of Tromsø. His scientific contributions span over 50 peer-reviewed articles, covering topics from parallel computing to synthetic health data interoperability. He emphasizes reproducibility and transparency in data management, exemplified by projects like Occode for historical data transcription and MORTAL language for cross-paradigm computing. In education, Bongo advocates for accessible teaching materials and has designed courses like INF-2202 Concurrent Programming, which utilize modern tools (e.g., Go language) and GitHub repositories. His teaching philosophy integrates practical coding with theoretical foundations, fostering student innovation and open-source collaboration.

Dongyoon LeeView profile

Associate Professor

• Computer Systems
• Software Reliability
• Software Security
• +9 more

Dongyoon Lee is an Associate Professor in the Department of Computer Science at Stony Brook University. His research focuses on building reliable, secure, and efficient software systems, spanning compiler design, operating systems, security, and hardware architecture. He holds a Ph.D. (2013) from the University of Michigan, Ann Arbor, and has held positions at Virginia Tech (2014-2019) and internships at Microsoft Research, NEC, and IBM. Education: Ph.D. in Computer Science & Engineering, University of Michigan, Ann Arbor (2013) M.S. in Computer Science & Engineering, University of Michigan, Ann Arbor (2009) B.S. in Electrical and Computer Engineering, Seoul National University (2004) Research Interests: Lee's work addresses challenges in software reliability (e.g., concurrency bugs, transient faults), security (e.g., memory safety, kernel permissions), and runtime systems (e.g., stream processing, distributed storage). He leads the COSY Lab (Compilers, Operating Systems, and Security), emphasizing cross-stack solutions. Recent Article Trends: Recent publications focus on homomorphic encryption compilers (e.g., DaCapo , HECATE ), energy-efficient systems ( Write-Light Cache ), and security tools like DevFuzz for device driver testing. Themes include optimizing performance-security trade-offs and addressing vulnerabilities in edge computing and embedded systems. Awards: Distinguished Paper Awards (ESEC/FSE 2018, ASE 2019), ICTAS Junior Faculty Award (2017), Google Research Award (2015), and ProQuest Distinguished Dissertation Award (2013). Advising & Grants: Supervises students in Ph.D., MS, and undergraduate programs. Secured grants including NSF SaTC funding for homomorphic encryption compilers and projects with the South Korean Agency for Defense Development (ADD). Leads research on ARM TrustZone security and energy harvesting systems. Labs/Teams: COSY Lab (Compilers, Operating Systems, and Security) at Stony Brook, collaborating on cross-disciplinary projects with industry and government partners.

Jordan KodnerView profile

Assistant Professor

• Computational Linguistics
• Language Acquisition
• Morphology
• +3 more

Jordan Kodner is an Assistant Professor in the Department of Linguistics at Stony Brook University, with affiliations to the Institute for Advanced Computational Science, Department of Computer Science, Institute for AI-Driven Discovery and Innovation, and the Natural Language Processing (NLP) group. He holds a PhD in Linguistics (2020) and a Master's in Computer and Information Science (2018) from the University of Pennsylvania. His research focuses on computational models of child language acquisition, particularly morphology, and its implications for NLP and language variation/change. He has worked on defense/medical projects at Raytheon BBN Technologies (2013-2015) and interned at Amazon Alexa AI in 2020. Research interests include computational linguistics, morphology, language acquisition, cognitive science of language, and low-resource NLP. He is writing a book on language change mechanisms grounded in child acquisition models. Teaching includes courses like LIN 260 (Language and Mind), LIN 330 (Language Acquisition), and LIN 537 (Computational Linguistics I). His work spans multiple languages (e.g., Arabic, Armenian, German, Korean, Spanish) and integrates findings from historical linguistics, sociolinguistics, and computational methods. Key contributions include studies on morphological learning, neural network limitations, and language change actuation via acquisition processes.

Christos PanayiotouView profile

Professor

• Modeling
• Control Systems
• Optimization
• +6 more

Christos Panayiotou is a Professor in the Electrical and Computer Engineering Department at the University of Cyprus (UCY) and serves as the Deputy Director of the KIOS Research and Innovation Center of Excellence. He holds a B.Sc. and Ph.D. in Electrical and Computer Engineering from the University of Massachusetts Amherst (1994 and 1999), as well as an MBA from the Isenberg School of Management. Prior to joining UCY in 2002, he was a Research Associate at Boston University's Center for Information and System Engineering. His research focuses on modeling, control, optimization, and performance evaluation of discrete event and hybrid systems, with applications in intelligent transportation systems, cyber-physical systems, machine learning, wireless networks, and smart buildings. His work has secured over €45 million in funding from national/EU agencies, governments, and private entities. He has published over 290 papers and received the 2014 Best Paper Award in Building and Environment . Editorial Roles : Associate Editor for IEEE Transactions on Intelligent Transportation Systems, IEEE Control Systems Society, and others. Leadership : General Chair of EWGT2020, Co-Chair of ECC2018, and Chair of IEEE Computational Intelligence Society subcommittees. Key projects include UAV swarm coordination for maritime surveillance, digital twin architectures for smart buildings, and resilient traffic demand management systems. His grants emphasize cyber-physical systems, energy grids, and disaster response technologies.