Find Academic Supervisors

• Autonomous Robot Systems
• Applied Ocean Science
• Quantum Engineering
• +4 more

Mohammad Javad Khojasteh is a Gleason Endowed Assistant Professor in the Department of Electrical and Microelectronic Engineering at the Kate Gleason College of Engineering, Rochester Institute of Technology (RIT). He directs the Khojasteh Autonomous Systems Laboratory (KAS Lab) and is affiliated with RIT's Global Cybersecurity Institute (GCI) and Center for Human-aware AI (CHAI). Before joining RIT, he held postdoctoral positions at Scripps Institution of Oceanography (MPL), MIT’s Department of Mechanical Engineering and LIDS, and Caltech’s CAST, collaborating with NASA JPL’s Team CoSTAR. Education: B.Sc. in Electrical Engineering and Mathematics, Sharif University of Technology (2015) M.Sc. in Electrical and Computer Engineering, UC San Diego (2017) Ph.D. in Electrical and Computer Engineering (Intelligent Systems, Robotics, and Control), UC San Diego (2019) Research Interests: Dr. Khojasteh focuses on autonomous systems, applying machine learning and control theory to enhance robotics, marine autonomous systems, and quantum engineering. His work spans cybersecurity in cyber-physical systems, data-driven optimization, and statistical signal processing. He emphasizes practical applications in ocean science and quantum technologies. Key Contributions: His research has been recognized with awards such as the Tammy L. Blair Student Paper Award. He explores topics like particle flow filters, event-triggered control, and secure autonomous navigation. His lab’s projects include developing robust algorithms for underwater robotics and quantum sensor networks. Advising & Grants: While specific grants are not detailed, his work indicates a focus on multi-agent systems, safety-critical control, and adversarial learning. He advises students in robotics and control systems, though no named advisees are listed. Labs & Teams: Director of KAS Lab, collaborating with GCI and CHAI on human-AI interaction and cybersecurity challenges in autonomous systems.

• Pervasive Computing
• Artificial Intelligence
• Cybersecurity
• +4 more

Nikhil Yadav, PhD, is an Associate Professor and Chair of the Computer Science Department at the University of San Diego's Shiley-Marcos School of Engineering. He holds a PhD from the University of Notre Dame, an MS from the University of Florida, and a BEng from the National University of Lesotho. His research focuses on pervasive computing, AI-driven context-aware systems, and healthcare technology innovation. Previously, Dr. Yadav served as computer science faculty at St. John's University in New York, where he led ABET accreditation efforts and developed internship programs. He currently advises Collidescope Inc. as their consulting CTO, focusing on social impact through ESG-oriented digital solutions. His industry experience includes software engineering roles and founding a startup leveraging speech biomarker analysis for mild traumatic brain injury (mTBI) assessment. Research themes span IoT security, healthcare informatics, and AI ethics. Notable projects include CRADLE—a psychophysiological analytics platform—and work on mobile health applications in underserved regions. His publications address edge AI deployment, cybersecurity in federated learning, and mental health prediction during public health crises. Teaching contributions include pioneering Python pedagogy in graduate data science curricula and integrating mobile healthcare projects in undergraduate courses. Professional service includes conference program committees and advocacy for secure ecological momentary assessments in clinical research.

Nandita VijaykumarView profile

Assistant Professor

• Computer Architecture
• Compilers
• Systems
• +5 more

Nandita Vijaykumar is an Assistant Professor in the Computer Science Department at the University of Toronto and the Department of Computer and Mathematical Sciences at the University of Toronto Scarborough. She leads the embARC Research Group, affiliated with the Vector Institute and Robotics Institute. Her work focuses on computer architecture, compilers, and systems, with a particular emphasis on the interplay between programming models, systems, and architectures. She holds a Ph.D. and M.S. from Carnegie Mellon University (2019), advised by Prof. Onur Mutlu and Prof. Phil Gibbons. She has held roles at Intel Labs, AMD, Microsoft, and Nvidia, and was a visiting student at ETH Zurich's Systems Group. Her research bridges computer architecture/systems with robotics, computer vision, and deep learning. Key projects include optimizing GPU algorithms, differential privacy in hardware, and 3D neural graphics. She mentors over 30 graduate and undergraduate students across Ph.D., MSc, and internship programs, fostering innovation in distributed systems and machine learning infrastructure. Nandita’s recent publications address challenges in differentiable rendering, distributed robotics, and energy-efficient computing. Her work on frameworks like DPWatch and DiffusionRenderer highlights her contributions to privacy-aware hardware and neural rendering. Collaborations with industry and academic institutions underscore her commitment to practical, cross-disciplinary solutions.

• Security and Privacy
• Systems and Networking
• Wireless Communication
• +3 more

Robin Hillary Kravets is a Professor in the Department of Computer Science at the College of Engineering, University of Illinois, where she investigates Security and Privacy and Systems and Networking . She holds a Ph.D. in Computer Science from the Georgia Institute of Technology (August 1999) and contributes to wireless IoT research through courses like CS 498 Wireless IoT Lab and ECE 439 Wireless Networks . Ph.D., Georgia Institute of Technology, August 1999 Research Interests Professor Kravets focuses on enhancing security and privacy in wireless networks, particularly addressing challenges in IoT environments . Her work spans device identification , MAC randomization tracking , energy-efficient protocols , and context-aware networking , with applications in smart retail, urban computing, and disaster response systems. Article Trends Her recent publications (2022–2024) emphasize Wi-Fi and BLE security , IoT privacy , and low-power networking . Key themes include device tracking mitigation , soil moisture sensing via LoRa , and energy-efficient protocols for dense networks . Scientific Awards Best Poster Award at ACM MobiCom 2024 Distinguished Reviewer at ACM MobiCom 2024 Advising and Collaborations She collaborates with PhD students Federico Cifuentes-Urtubey and Jay Shenoy on wireless security projects, contributing to awards at ACM MobiCom 2024. Her work often intersects with IoT , mobile sensing , and disruption-tolerant networks .

Romit Roy ChoudhuryView profile

Professor

• Machine Learning
• Signal Processing
• Generative Models
• +6 more

Romit Roy Choudhury is the Gilmore Family Endowed Professor of Electrical and Computer Engineering (ECE) at the University of Illinois at Urbana-Champaign (UIUC) and an Amazon Scholar since 2022. He earned his Ph.D. in Computer Science from UIUC in 2006 and previously held positions at Duke University, including Associate Professor (2011–2013) and Assistant Professor (2006–2011). His research spans generative models, signal processing, and wearable computing, with a focus on audio denoising, source separation, and spatial inference. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign, 2006. Research Interests: Generative Models, Machine Learning, Sensing, Inverse Problems, Wearable Devices, Localization, and Acoustic Sensing. His lab, Signals and Inference Research Group (SiNRG), explores innovations in audio and sensor-based systems. Publications: Over 50+ peer-reviewed articles, including impactful work on inaudible sound attacks (MobiSys 2017 Best Paper), noise cancellation via IoT (SIGCOMM 2018), and earable computing (MobiCom 2020). Awards: ACM Sigmobile Rockstar Award (2015), IEEE Fellow (2019), NSF CAREER Award (2007), and multiple best-paper recognitions. Teaching: Courses include ECE 438 (Communication Networks), ECE 434 (Real-World Algorithms for IoT), and ECE 101 (Intro to Computing). Labs/Teams: SiNRG at UIUC, collaborations with Amazon, Samsung AI, Intel, and Microsoft Research.

Hui GuanView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Programming Systems
• +3 more

Hui Guan is an Assistant Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. Currently on leave, she works at Amazon AWS developing LLM systems. She holds a PhD in Electrical Engineering from North Carolina State University (2020) and is a member of the PLASMA lab at UMass. Education: PhD in Electrical Engineering, North Carolina State University, 2020 Research Interests: Focuses on machine learning systems, optimizing deep multitask and graph learning. Aims to improve efficiency, scalability, and reliability of ML through system innovations. Leverages principles like composability and locality awareness to democratize ML applications. Awards: NSF CAREER Award (2024) Amazon Research Award (2022) NCSU ECE Distinguished Dissertation Award (2020) IBM PhD Fellowship (2015-2018) Grants and Projects: Includes NSF support for projects like Adaptive Deep Learning Systems and Memory-Driven Collaboration for Embedded Systems. Received grants from Adobe and Dolby. Students and Advising: Advises PhD students including Lijun Zhang, Kunjal Panchal, and Qizheng Yang. Collaborates with students from other groups like Sohaib Ahmad. Labs and Teams: Active member of the PLASMA lab, focusing on programming languages and systems research.

• Smart Cities
• Data Analytics
• Artificial Intelligence
• +6 more

Dr. Anish Jindal is an Associate Professor at Durham University's Department of Computer Science, with affiliations to the Durham Energy Institute and visiting fellowships at Princeton University. His research focuses on smart cities, energy optimization, AI, and cyber-physical systems. He holds a Ph.D. from India (2018) and has authored/co-authored over 100 publications in top-tier journals/conferences like IEEE Transactions and ACM MobiHoc. Notable awards include IEEE TCSC's Outstanding Ph.D. Dissertation Award (2019) and the IEEE Communication Society's Outstanding Young Researcher Award (2019). Research interests include resource-aware computing, programmable networking, wireless security, and energy-efficient AI. He leads major grants such as the £3M EPSRC CHEDDAR hub (EP/X040518/1) and the £10M National Edge AI Hub (EP/Y028813/1). His work spans 6G networks, edge computing, and IoT applications in smart grids and healthcare. Recent articles emphasize digital twins for IoT security, energy-efficient AI frameworks, and UAV-assisted vehicular edge computing. He actively organizes workshops like IEEE CSR's Data Science for Cyber Security and serves on editorial boards of journals like IET Smart Cities and Software: Practice and Experience.

Julieanna Powell-TurnerView profile

Professor

• Environmental Sustainability
• Climate Change Resilience
• Resource Security
• +2 more

Julieanna Powell-Turner is Professor of Environmental Organisational Sustainability and Associate Dean for Research and Innovation at the University of Chester's Faculty of Science, Business and Enterprise. She leads the Centre for Research into Environmental and Sustainable Transitions (CREST), driving interdisciplinary work on climate resilience, resource security, and social cohesion aligned with UN SDGs. With 20+ years in sustainability research, she has secured significant UKRI funding and supervised doctoral projects in decarbonization and green skills. Her recent publications focus on climate action governance (2025), corporate sustainability barriers (2025), and semantic access controls for healthcare systems (2022-2024). Powell-Turner oversees institutional research frameworks including REF/KEF and manages postgraduate programs, emphasizing participatory action research and cross-sector partnerships.

• Sustainable and Resilient Environments
• Workplace Safety and Health Risk Monitoring
• Digital Transformation in Project Management
• +6 more

Dr. Hoonyong Lee is a Lecturer at the School of Project Management, Faculty of Engineering, University of Sydney. His research focuses on building sustainable and resilient environments through advanced safety and health risk management frameworks. PhD in Construction Science, Texas A&M University Postdoctoral Fellow, University of Michigan Member, Net Zero Institute and Brain and Mind Centre Research spans human sensing technologies , data analytics , and behavioral intervention design , with applications in: Construction Safety Management Smart Home Healthcare Fall Risk Assessment Household Energy Conservation His work combines wearable biosensors , Wi-Fi CSI , and machine learning for proactive risk identification. Recent projects focus on team cohesion in project management using physiological data analytics. 2024 Best Paper Award, Construction Research Congress 2024 Best Data Award Honorary Mention, ASCE DSA Committee Multiple award-winning publications (2012–2024) Teaching includes Project Acceptance and Project Planning courses. Leads Team L in developing adaptive intervention systems.

Amanda HoviousView profile

Assistant Professor

• Information Science
• Library Science
• Information Literacy
• +5 more

Amanda Hovious is an Assistant Professor in the School of Library and Information Management (SLIM) at Emporia State University. She holds a PhD in Information Science from the University of North Texas, an MS in Instructional Design and Technology from the University of North Dakota, an MLIS from the University of Wisconsin-Milwaukee, and a BMus in Music with business electives from DePaul University. Her research focuses on information literacy, digital equity, and the evolving roles of libraries in community resilience. Key areas include analyzing small/rural library infrastructures, addressing accessibility inequities in digital databases, and exploring transmedia storytelling's impact on education. She critiques modern information landscapes through studies on digital ethnography, AI-generated content ethics, and OER adoption barriers. Her work bridges theory and practice, emphasizing practical library instruction strategies and measurable educational outcomes. Notable contributions include frameworks for data wellness, multimodal readability metrics, and resilience research with Kansas public libraries. Hovious advocates for librarian roles in fostering critical thinking and ethical engagement with information in digital environments. Her articles consistently explore intersections between technology, education, and societal impacts, with recent attention to misinformation dynamics in grassroots movements and AI's implications for information creation standards.

Dejene DedefaView profile

Assistant Professor

• Computer Networking
• Wireless Networks
• Information Privacy
• +3 more

Dejene Dedefa is the Chair and Assistant Professor of the Computer Science Department at Lincoln University. His research focuses on wireless networking protocols, information privacy in low-income regions, and context-aware systems design. He has developed novel localization algorithms for wireless networks and contributed to energy-efficient routing protocols for MANET environments. His work intersects computer science with socio-technical challenges, particularly in African contexts. Key contributions include frameworks for cross-platform UI delegation, models analyzing privacy concerns in social networks, and data-driven approaches to road safety improvement through accident pattern analysis. Dr. Dedefa's publications span 2006-2015, emphasizing interdisciplinary solutions in pervasive computing, data mining, and network optimization. His research methodologies combine algorithmic innovation with cultural and behavioral analysis to address real-world challenges in technology deployment.

Collin McMillanView profile

Associate Professor

• Software Engineering
• Natural Language Processing
• Program Comprehension
• +3 more

Collin McMillan, Ph.D., is an Associate Professor of Computer Science at the University of Notre Dame's College of Engineering. He earned his Ph.D. from William & Mary under Denys Poshyvanyk. His research focuses on software engineering and natural language processing, particularly in automated documentation generation, program comprehension, and human-AI collaboration in code analysis. Education: Ph.D. in Computer Science (William & Mary), advised by Denys Poshyvanyk. His work integrates eye-tracking studies, neural networks, and graph-based models to improve code summarization and developer tooling. Research interests include source code summarization, developer behavior analysis, privacy behavior detection in Android apps, and tools for program comprehension. His lab, the Automatic Program Comprehension Lab (APCL), develops datasets and tools like CodeGRITS, available via Huggingface and GitHub. Publications span 2007–2025, emphasizing developer-centric AI models, code summarization techniques, and empirical studies on programmer attention. Key themes include context-aware models, eye-tracking integration, and LLM applications in software engineering. Awards: ASEE Teaching Award (2020), NSF CAREER Award (2015), Distinguished Paper Award (2021). Grants: NSF funding for AI/SE integration, totaling over $2.5M since 2015. Service: Associate Editor for TSE and EMSE, program committee roles at ICSE, FSE, and ASE. Current students include Maria Dhakal (2023), Robert Wallace (2023), and Chia-Yi Su (2022). Graduates have moved to faculty positions (e.g., Clemson University) and industry roles at Google, Qualtrics, and Ascend Health.

Jeyamohan NeeraView profile

Assistant Professor

• Privacy Preserving Technologies
• Online Harms
• Cybersecurity Threats
• +4 more

Jeyamohan Neera is an Assistant Professor and Programme Leader for the Networks and Cybersecurity programme at Northumbria University. Her research focuses on privacy-preserving technologies, online harms, and cybersecurity threats such as intrusion detection, APTs, social engineering, and mis/disinformation. She leads the institutional role as PI for the SPRITE+ funded project Fact-Chkd and contributes as Co-Investigator to EU-funded projects HarmonicAI (exploring privacy-aware AI models for digital health) and Digihealth-Asia (enhancing digital health capacities in Asia). Her academic background includes a Computing PhD (2022) and membership in Northumbria’s Cybernets Research Group. She is an Expert Fellow for the Security Privacy Identity Trust in the Digital Economy (SPRITE+) initiative. Key research interests span privacy-enhancing technologies, AI-driven cybersecurity solutions, and mitigating adversarial misuse of generative AI/LLMs for disinformation and social engineering attacks. Her work addresses real-world challenges such as securing mobile payment systems, improving recommendation system privacy, and developing explainable AI models for ransomware detection. Recent projects demonstrate her expertise in interdisciplinary cybersecurity solutions, combining technical innovation with societal impact. Supervision includes PhD candidate Peter Smith exploring federated learning applications in balancing cybersecurity datasets. Her contributions to EU-funded initiatives highlight her role in advancing digital health security and fostering international academic collaboration through capacity-building programs.

Heng FanView profile

Assistant Professor

• Computer Vision
• Video Analysis
• Robotics
• +2 more

Heng Fan is an Assistant Professor in the Department of Computer Science and Engineering at the University of North Texas, with affiliated faculty status in the Department of Data Science. He leads the LLaVi Lab and holds editorial positions at Pattern Recognition and serves as an Area Chair for major conferences including ICCV and NeurIPS. He earned his B.S. from Huazhong Agricultural University (2013) and Ph.D. from Stony Brook University (2021), advised by Professor Haibin Ling. His research focuses on: Computer vision, especially video analysis using vision-language integration Robotics with an emphasis on perception systems Medical image analysis for diagnostic applications Recent publications demonstrate strong emphasis on transformer architectures for spatio-temporal video understanding, 3D object detection in autonomous systems, and multimodal AI applications. Dominant themes include UAV tracking, video grounding, and privacy-preserving NLP, reflecting cross-disciplinary innovation. He actively recruits PhD/master's students for computer/robotic vision projects and leads the LLaVi Lab, focusing on cutting-edge perception systems.

Yan HuangView profile

Professor

• Spatio-Temporal Databases
• Geo-Stream Data Processing
• Smart Transportation
• +3 more

Yan Huang is a Regents Professor in the Department of Computer Science and Engineering at the University of North Texas (UNT). She holds ACM Distinguished Member and UNT Decker Scholar titles. Her roles include Associate Dean for Research and Graduate Studies (2016-2019), Interim Dean of the College of Engineering (2018), Senior Associate Dean (2019-2021), and Interim Chair of Computer Science and Engineering (2020-2021). She has served as a visiting scholar at Microsoft Research Asia and Fudan University. Education: B.S. in Computer Science from Peking University (1997) and Ph.D. in Computer Science from the University of Minnesota (2003). Research interests focus on spatio-temporal databases, geo-stream data processing, smart transportation, and location-based social networks. Her work is funded by NSF, DoD, Texas Department of Transportation, and Oak Ridge National Lab. Notable achievements include a $3.76M grant from the Army Research Lab (2023). Key awards include the Best Paper Award at IEEE IRI 2023 (student work) and recognition as a Regents Professor (2021). She advises multiple Ph.D. students and oversees a lab (DP F205).