Find Academic Supervisors

Lawrence SassView profile

Professor

• Digital Fabrication
• Low-Cost Housing
• Construction Automation
• +3 more

Lawrence Sass is a Professor and Chair of the Computation Group in the Department of Architecture at MIT's School of Architecture + Planning. His research focuses on design innovation using digital fabrication, particularly in low-cost housing and construction automation. He pioneered methods to reduce home construction steps through computational tools, including the concept of Snap Assembly building systems showcased at MoMA in 2008. His work has inspired startups in digitally fabricated housing. B.Arch from Pratt Institute (1990) Master’s (1994) and PhD (2000) from MIT Research interests include sustainable construction techniques, 3D printing in architecture, and material optimization. He co-founded LuBan3D, a software system enabling large-scale 3D modeling with affordable tools. Current projects explore AI and robotic systems for home production. Awards include the prestigious MacVicar Faculty Fellow recognition. His teaching includes courses like Design Computation (4.500) and Tiny Fab (4.501), emphasizing computational methods in architectural design. Collaborations include work with Singapore-based researcher Prof. Lujie Chen on large-scale fabrication software. His lab focuses on integrating digital tools into physical production workflows, aiming to democratize construction through accessible fabrication technologies.

Kimberlee KearfottView profile

Professor

• Radiation Protection
• Nuclear Medicine
• Medical Physics
• +3 more

Kimberlee Kearfott, Sc.D., is a Professor in the Department of Nuclear Engineering and Radiological Sciences at the University of Michigan. Her primary affiliation is with the College of Engineering, and she holds an additional role as Affiliate Faculty in Biomedical Engineering (BME). Her research focuses on radiation protection, nuclear medicine, medical physics, and biomedical imaging. Key areas include radon gas dynamics, dosimetry techniques, environmental radiation monitoring, and the development of radiation-aware technologies like drones and weather stations. Her work spans theoretical and applied domains, including algorithm development for anomaly detection in radon time series data, advanced imaging systems, and radiation source mapping. She has contributed to the design of cost-effective radiation measurement instruments and systems for real-time environmental monitoring. Notable projects include the creation of an Intelligent Radiation Awareness Drone and a Low-cost Radiation Weather Station. Dr. Kearfott’s expertise also extends to radiation safety protocols, quality control in dosimetry calibration, and the application of machine learning to thermoluminescent dosimeter analysis. Her research has addressed critical issues such as earthquake prediction through radon gas analysis and sterilization techniques for SARS-CoV-2-contaminated equipment. Her laboratory focuses on interdisciplinary projects at the intersection of nuclear engineering, biomedical sciences, and environmental science. Collaborations involve both academic and industrial partners, emphasizing practical solutions for radiation-related challenges in healthcare, environmental safety, and homeland security.

• Machine Learning
• Artificial Intelligence
• Computer Science
• +3 more

Prof. Felix Bießmann holds a professorship in Computer Science and Media at Berlin University of Applied Sciences' Department VI. His research focuses on machine learning applications in diverse fields including healthcare, urban planning, environmental science, and robotics through his Cognitive Algorithms Lab. He teaches courses such as Machine Learning, Deep Learning, and Data Science Workflows, alongside roles at TU Berlin and Korea University. Education: PhD (Dr. rer. nat.) in Natural Sciences Research interests span machine learning theory and practical implementations across domains like computer vision, generative AI, and sensor data analysis. His work addresses challenges in automated systems, healthcare monitoring, and sustainable technologies. Recent student theses explore topics like license plate recognition, adaptive game soundtracks, and bird song detection using TinyML. Collaborations include projects with the Charité Berlin and Robert-Koch Institute. Lab: Cognitive Algorithms Lab (developing machine learning methods/applications) Contact: felix.biessmann@bht-berlin.de | Office D138, Berlin University of Applied Sciences.

Mauricio de Campos PorathView profile

Professor

• Coordinate Measuring Technology
• Quality Management
• Production Engineering
• +7 more

Prof. Mauricio de Campos Porath is a Professor of Production Engineering and Measuring Technology at the Hamburg University of Applied Sciences (HAW Hamburg), Department of Mechanical Engineering and Production. He holds a Dr.-Ing. from RWTH Aachen University (2009), M.Sc. in Production Engineering from RWTH Aachen (2004), and B.Sc. in Mechanical Engineering from Universidade Federal de Santa Catarina (UFSC), Brazil (2002). His research focuses on precision metrology, including coordinate measuring technology, industrial computed tomography, and uncertainty analysis in manufacturing processes. Key roles include serving as Deputy Study Program Coordinator for the B.Eng. in Mechanical Engineering (Production Technology) at the Shanghai-Hamburg College (since 2024), Member of the Faculty Council (TI Faculty), and Reviewer for journals like Precision Engineering and Measurement Science and Technology. He has extensive industry experience, including roles at Carl Zeiss IMT GmbH and as a Measurement Engineer at CERTI Brazil. Research interests span advanced measurement techniques for industrial applications, robot calibration, and quality management. His work emphasizes bridging theoretical metrology with practical manufacturing challenges, particularly in additive manufacturing and shipbuilding assembly processes. Publications highlight contributions to calibration methodologies, uncertainty quantification, and precision measurement systems. He actively participates in international conferences and academic reform initiatives, reflecting his commitment to advancing technical education and industrial metrology standards.

• Control Engineering
• Automation
• Model Predictive Control
• +5 more

Martin Steinberger is an Associate Professor at the Institute of Control and Automation (IRT) at Graz University of Technology (TU Graz). His work focuses on advanced control systems, networked control, model predictive control (MPC), and automation in manufacturing and autonomous systems. He leads research into real-time optimization, fault diagnosis, and safety-critical applications in industries like pharmaceuticals and automotive. Research interests include: Networked Control Systems Model-Based Control Autonomous Vehicle Trajectory Planning Process Automation Robotics and Industrial Automation His work bridges theoretical control engineering with practical applications in manufacturing lines, chemical processes, and autonomous driving. Recent studies emphasize digital real-time release testing for pharmaceuticals, universal control concepts for manufacturing systems, and safety-aware trajectory optimization for automated vehicles. His publications frequently address challenges in time-varying delays, packet loss mitigation, and robust observer design for nonlinear systems. Steinberger collaborates on EU-funded projects and regularly contributes to conferences like the International Workshop on Variable Structure Systems. His research often involves experimental validation, as seen in work with compact pharmaceutical manufacturing setups and small-scale autonomous vehicle testing platforms.

Chunming QiaoView profile

Professor

• Cyber-Physical Systems
• Optical Networks
• Wireless Networks
• +6 more

Dr. Chunming Qiao is a SUNY Distinguished Professor and Chair of the Department of Computer Science and Engineering at the University at Buffalo (SUNY) , leading the Lab for Advanced Network Design, Evaluation and Research (LANDR) since 1993. His work spans cyber-physical systems , optical networks , and Internet of Things (IoT) , with a focus on safety, reliability, and protocol design. Education: PhD in Computer Science from the University of Pittsburgh (1993) BS in Computer Science and Engineering from the University of Science and Technology of China (1985) Dr. Qiao’s research interests combine theoretical and applied network design, including autonomous vehicles , quantum computing , and cloud services . He pioneered optical burst switching (OBS) and iCAR systems for wireless convergence, cited in BusinessWeek and Wireless Europe . His recent publications emphasize quantum networking , federated learning , and autonomous driving security , with projects on entanglement routing , edge inference optimization , and LiDAR adversarial attacks . Articles span IEEE and ACM venues , and include best paper awards . Scientific Awards: TC-CSR Distinguished Technical Achievement Award (2015) SUNY Chancellor's Award for Excellence (2013) IEEE Fellow (2009) UB Exceptional Scholar-Sustained Achievement Award (2005) Dr. Qiao has secured over two dozen NSF grants and collaborations with Google , Cisco , and NEC Labs . His 7 US patents and consulting experience highlight his industry impact, while his editorial roles and conference leadership underscore academic influence. He actively contributes to multi-disciplinary research through the New York State Center of Excellence in Bioinformatics and Life Sciences and CEDAR , advancing high-performance computing and document analysis .

Sheldon AndrewsView profile

Associate Professor

• Real-time physics simulation
• Articulated mechanism simulation
• 3D character animation
• +5 more

Sheldon Andrews is an Associate Professor of Software Engineering and IT at École de technologie supérieure (ETS) in Montreal, Canada, with an adjunct appointment in Computer Science at McGill University. He is a member of the Multimedia Research Laboratory and has established himself as a leading researcher in physics-based computer animation and simulation. Andrews earned his Ph.D. in Computer Science from McGill University (2015), MASc in Electrical and Computer Engineering from the University of Ottawa (2007), and B.Eng. in Computer Engineering from Memorial University (2004). His academic journey reflects a strong foundation in both theoretical and applied aspects of computer engineering and graphics. His research focuses on real-time physics simulation, articulated mechanism simulation, 3D character animation, motion capture, computational contact mechanics, and virtual environment modeling. Andrews' work bridges the gap between theoretical physics and practical applications in computer graphics, with particular emphasis on creating physically plausible animations that can run in real-time. His research has significant implications for video games, virtual reality, and robotics applications. Analysis of his recent publications (2022-2025) reveals a strong trend toward increasingly sophisticated physics-based character animation techniques, with growing integration of machine learning approaches. His work spans multiple subfields including collision detection, deformable object simulation, vehicle physics, and reinforcement learning for character control, demonstrating both breadth and depth in his research program. VRIPHYS 2012 best paper award for 'Policies for goal directed multi-finger manipulation' Andrews has advised numerous graduate students through their PhD and Master's degrees, with many going on to positions at major companies like DNEG, CM Labs Simulations, and AMD. His professional service is extensive, having served as Program Chair for SCA 2025 and MIG 2024, Conference Chair for I3D 2019, and on program committees for major conferences including SIGGRAPH, SCA, and MIG for multiple years. He has also been active in the Montreal SIGGRAPH Chapter as Secretary from 2018-2021. As a core member of the Multimedia Research Laboratory, Andrews collaborates with researchers across multiple disciplines to advance the state of the art in physics-based simulation. His lab maintains strong industry connections, including a visiting researcher position at Roblox Research, ensuring that theoretical advances translate to practical applications in gaming and virtual environments.

Eunsik KimView profile

Professor

• Ergonomics
• Biomechanics
• Human Factors Engineering
• +5 more

Eunsik Kim is a Professor at the University of Windsor , holding a position in the Department of Mechanical, Automotive, and Materials Engineering within the Faculty of Engineering . His research focuses on ergonomics, occupational safety, and biomechanics, with applications in automotive design, workplace safety, and human-machine interaction. He leads the Occupational Safety and Ergonomics research lab , where students explore real-world solutions for challenges such as driver fatigue, musculoskeletal risks in manual labor, and automation in agriculture. Key research interests include: Ergonomics : Optimizing workstation designs, automotive seating, and anti-vibration tools Biomechanics : Analyzing human posture, musculoskeletal loading, and workplace ergonomics Machine Learning : Developing AI-driven systems for posture recognition, workload prediction, and task automation Autonomous Vehicles : Investigating human factors in driving automation and takeover scenarios Education Innovation : Gamification in engineering labs to enhance student motivation and learning Collaborations include projects with Ewha Womans University (South Korea) on autonomous vehicle comfort and a prototype robotic system for mushroom harvesting. His work often bridges theoretical research with practical applications, addressing both industry and academic challenges. Awards and Recognition: Students Ilfeoma Michael and Elnaz Akhavan Rezaee received accolades at the Association of Canadian Ergonomists conference for their lab research Advising and Grants: Professor Kim mentors students in interdisciplinary projects, focusing on ergonomics, robotics, and AI. His research has led to innovations in workplace safety and automated systems, supported by institutional and collaborative funding. Labs/Teams: The Occupational Safety and Ergonomics Lab and partnerships with South Korean institutions drive cutting-edge studies in automotive safety, robotics, and human factors engineering.

Peter Groot KoerkampView profile

Professor

• Agricultural Engineering
• Environmental Engineering
• Animal Production Systems
• +7 more

Prof. Peter Groot Koerkamp is a Professor and Managing Chairholder at the Agricultural Biosystems Engineering group at Wageningen University. His expertise spans agricultural engineering, environmental systems, and sustainable livestock production. He holds an MSc (1990) and PhD (1998) from Wageningen University, with a focus on ammonia emissions from poultry systems. He has held roles as a researcher, project manager, and senior scientist across multiple institutes before joining Wageningen University in 2005. His research emphasizes sustainable agricultural systems, including manure/nutrient management, gaseous emissions, and animal welfare. He leads projects on methane monitoring in dairy cows, climate control in livestock housing, and bioenergy from segregated excreta. He supervises over 50 PhD students and collaborates on initiatives like the National Growth Fund’s regenerative agriculture projects. Notable projects include Synergia (optimizing dairy systems) and collaborations with the European Society of Agricultural Engineers (EurAgEng). His work integrates environmental engineering with technological innovation, addressing challenges like particulate matter reduction in poultry houses and precision livestock farming.

Jette ErnstView profile

Associate Professor

• Digitalization and robotization in hospitals
• Professional identity and boundary constructions in healthcare
• Performance management and work monitoration
• +3 more

Jette Ernst is an Associate Professor at Roskilde University's Department of Social Sciences and Business, specializing in organizational sociology and management studies. Her research focuses on the social and cultural dimensions of organizational change, particularly in healthcare settings, addressing topics like digitalization, robotics, professional identity, and power dynamics. She holds a PhD and multiple advanced degrees in business and communication studies. Education: PhD, Cand.Negot, Master of Science in Business (Language and Culture with English specialization). Research interests emphasize how automation and organizational restructuring impact healthcare work, employee responses, and professional boundaries. She employs qualitative methods including ethnography and discourse analysis, drawing on Bourdieu's practice theory and framing theory. Key themes include robotization in hospitals, merger processes, and performance management. Notable projects include leading the Independent Research Fund Denmark-funded 'Robot technology in hospitals' (2023-2026) and Helsefonden's 'Human and robot exchanges in Danish hospitals' (2020-2022). Recent publications explore temporal strategies in automation and labor dynamics in automated healthcare systems. Awards: Best Developmental Paper Award (2024), EGOS 'That's Interesting' Award (2017), Emerald/EFMD Doctoral Research Award (2018) Teaching: Courses in organization theory, leadership, HRM, and research methodology Supervision: Active mentor for academic theses at all levels Labs/Teams: Crossroads of Care and Social Reproduction (CARE) research group

Prasad TadepalliView profile

Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +5 more

Prasad Tadepalli is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University, serving as the AI Graduate Program Director. He is affiliated with the Collaborative Robotics and Intelligent Systems Institute. His expertise spans artificial intelligence, machine learning, reinforcement learning, and automated planning, with impactful contributions to explainable AI and natural language processing. Tadepalli holds a Ph.D. from Rutgers University and M.Tech/B.Tech degrees from Indian institutions. He has authored over 100 papers, organized international conferences, and received awards such as the AAAI Outstanding Paper Award (2013) and ICAPS Best Student Paper (2009). Education: Ph.D. (Rutgers University, 1990), M.Tech (IIT Madras, 1981), B.Tech (Regional Engineering College, 1979) His research focuses on advancing AI through techniques like relational planning, reinforcement learning, and interpretable models. Recent work includes integrating planning and RL for multiagent systems and developing explainable models via tree ensemble compression. His articles highlight contributions to time-series imputation, adversarial attacks on bandits, and chess rating estimation using CNN-LSTM networks. Awards: AAAI Outstanding Paper Award (2013), ICAPS Best Student Paper (2009) Tadepalli emphasizes independent thinking in students and has advised numerous researchers. His work bridges theoretical AI with practical applications, such as robotics and data-driven decision-making.

Lilit YeghiazarianView profile

Associate Professor

• Environmental Engineering
• Water Resource Management
• Porous Media
• +5 more

Lilit Yeghiazarian is an Associate Professor at the University of Cincinnati’s School of Energy, Environmental, Biological & Medical Engineering and the Department of Biomedical, Chemical & Environmental Engineering. She holds a Ph.D. in Biological Engineering from Cornell University (2001), an M.S. in Industrial Engineering from the American University of Armenia (1995), and a B.S. in Automation and Control (Summa Cum Laude) from Yerevan Polytechnic Institute (1992). Her research focuses on Multifunctional advanced materials and environmental sensing Biomimetic robotics and autonomous sensors Porous media flow simulation and design Watershed sustainability and stochastic contaminant transport modeling Her work bridges microscopic and macroscopic scales, integrating stochastic systems, control theory, and environmental engineering for applications like urban flood management and pathogen transport. Key contributions include the Urban Flooding Open Knowledge Network (UF-OKN) , a framework for real-time flood forecasting and risk communication, and the Pore Topology Method for porous material design. She has secured over $10 million in federal grants, including NSF CAREER and RAISE awards. Awards include the Ruth L. Kirschstein National Research Service Award (2006–2008) and Alpha Epsilon Honor Society (1998). She serves on multiple editorial and review boards, including the Water Environment Research Foundation.

Cornelia Della CasaView profile

Researcher

• Robotics
• Autonomous Systems
• Healthcare Technology
• +3 more

Cornelia Della Casa is a Researcher affiliated with the Professorship for Autonomous Systems at ETH Zürich's Department of Mechanical and Process Engineering. Her work focuses on advancing robotics and autonomous systems, with notable involvement in European Commission-funded projects such as HERON (2021-2025), AERO-TRAIN (2021-2024), and HARMONY (2021-2024). These projects address challenges in robotic maintenance, healthcare assistance, and infrastructure maintenance. Key projects include developing autonomous robotic platforms for roadworks maintenance (HERON), training next-gen European infrastructure technicians (AERO-TRAIN), and enhancing healthcare through robotic mobile manipulation (HARMONY). She has also contributed to earlier initiatives like CROWDBOT (2018-2021) on safe robot navigation in crowds and V-Charge (2011-2015) for autonomous e-mobility charging. No scientific awards or specific publications are explicitly listed, though her extensive project portfolio underscores her research contributions in robotics and automation.

Christian MuiseView profile

Assistant Professor

• Artificial Intelligence
• Automated Planning
• Machine Learning
• +6 more

Christian Muise is an Assistant Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. He holds a PhD (2014) in Artificial Intelligence from the University of Toronto, where he was advised by Sheila McIlraith and J. Christopher Beck. His research focuses on automated planning under uncertainty, combining planning with learning for applications like goal-oriented dialogue systems and multi-agent coordination. He previously held postdoctoral roles at the University of Melbourne's Agentlab and MIT's CSAIL, and was a Research Staff Member at the MIT-IBM Watson AI Lab. Education: PhD in Artificial Intelligence, University of Toronto (2014) MSc in Computer Science, University of Toronto (2009) BSc in Computer Science, Carleton University (2007) Research Interests: His work bridges automated planning and machine learning, emphasizing robustness in uncertain environments. Key areas include non-deterministic planning, model acquisition with large language models (LLMs), and human-aware planning. He explores applications in healthcare (e.g., treatment response prediction), robotics (autonomous navigation), and dialogue systems for safety-critical domains. Current projects include developing explainable planning systems and mitigating bias in AI decision-making. Awards: Scotiabank Scholar (Scotiabank Centre for Customer Analytics) Advising & Labs: Leads the Mu Lab, supervising PhD and Master's students in topics like model acquisition, dialogue systems, and planning bias. Active in open-source tools (e.g., L2P, MACQ library) to democratize planning research. Collaborates on projects like PRP Rebooted and FixMyPlan to advance FOND planning and LLM integration. Labs/Teams: Mu Lab at Queen’s University, focusing on planning under uncertainty, AI safety, and neuro-symbolic systems.

Mahdi JampourView profile

Researcher

• Computer Vision
• Artificial Intelligence
• Digital Humanities
• +3 more

Dr. Mahdi Jampour is a Researcher at the Centre for the Study of Manuscript Cultures (CSMC), University of Hamburg, and a member of the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA). He holds a Ph.D. in Computer Science (Artificial Intelligence) from Graz University of Technology (2016), with postdoctoral research at Iran Telecommunication Research Center (ITRC) (2016–2017). He previously served as Assistant Professor at Quchan University of Technology (2017–2024) and led Project RFA05 (2022–2025) focusing on visual pattern similarity in written artefacts. Education: Ph.D. in Computer Science (Artificial Intelligence), TU Graz, Austria (2016) Postdoctoral Fellowship, ITRC, Iran (2016–2017) Assistant Professor, Quchan University of Technology (2017–2024) Research Interests: Dr. Jampour specializes in applying AI and computer vision to cultural heritage preservation, including palimpsest analysis, historical document digitization, and pattern recognition. His work integrates generative models, deep learning, and semi-supervised methods to address challenges in manuscript analysis and multispectral imaging. Publications Trends: Recent work focuses on generative AI for palimpsest deciphering, dataset creation for sports and cultural heritage analysis, and facial expression recognition surveys. His articles bridge computer science with digital humanities, emphasizing cultural artifact preservation through technological innovation. Awards: Kazemi-Ashtiani Award (2019) Chamran Award (2017) KUWI Prize (2015) Marshal Plan Fellowship (2015) Best MSc Thesis Award (2009) Advising & Grants: Led UWA’s Project RFA05 (2022–2025) and contributed to international preservation initiatives like the Timbuktu Manuscript Training Project. His research is supported by grants from the Iran National Elites Foundation and the Iranian Ministry of Science. Labs & Collaborations: Active in CSMC’s labs, including the Written Artefact Profiling Guide and Mobile Lab Container projects. Collaborates with institutions globally on digitization and cultural heritage safeguarding.