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Franklyn TurbakView profile

Associate Professor

• Programming Languages
• Blocks Programming
• Computer Science Education
• +3 more

Franklyn Turbak is an Associate Professor in the Computer Science Department at Wellesley College, where he has been teaching since 1995. His work focuses on programs and programming languages, which forms the foundation of both his teaching and research activities. He is a member of the core development team for MIT App Inventor and leads the TinkerBlocks research group at Wellesley College. Dr. Turbak received his educational credentials from the Massachusetts Institute of Technology, earning his B.S., M.S., and Ph.D. in Computer Science. Dr. Turbak's primary research interest is in blocks programming languages, particularly MIT App Inventor, where he has been actively working since 2009. His research explores how to make programming more accessible through visual representations of programs and the visualization of computational processes. He is particularly interested in democratizing computing by creating blocks programming languages that are more expressive and easier to use. His work spans programming language design, analysis, and implementation, with a strong focus on educational applications. His recent publications demonstrate a consistent focus on enhancing blocks programming environments, particularly App Inventor. The research shows trends toward improving abstraction mechanisms in visual programming, analyzing user behavior in blocks programming environments, and exploring collaborative aspects of visual programming. His work bridges the gap between theoretical programming language concepts and practical educational applications. Co-author of the textbook "Design Concepts in Programming Languages" Lead PI for the NSF-funded "Computational Thinking Through Mobile Computing" project (2012-2016) Organized four major blocks programming workshops (2015, 2017, 2018, 2019) Dr. Turbak has been actively involved in mentoring students and developing innovative courses. He created and taught the "Inventing Mobile Apps" course, which allowed students without prior programming experience to design and build Android apps using App Inventor. He has taught courses including Introduction to Programming and Problem Solving (CS111), Foundations of Computer Systems (CS240), Data Structures (CS230), Fundamental Algorithms (CS231), Programming Languages (CS251), and Compiler Design (CS301). Together with Robbie Berg from the Wellesley Physics Department, he developed the "Robotic Design Studio" course for introducing liberal arts students to engineering. At Wellesley, Dr. Turbak leads the TinkerBlocks research group, which focuses on improving and studying MIT App Inventor. He has organized four major blocks programming research workshops since 2015 to build a global community around this area of research: the 2015 and 2017 IEEE Blocks and Beyond Workshops, the 2018 BLOCKS+ ACM SPLASH Workshop, and the 2019 Blocks and Beyond Workshop.

Hazem TorfahView profile

Assistant Professor

• cyber-physical systems
• formal verification
• runtime monitoring
• +5 more

Hazem Torfah is a tenure-track Assistant Professor at the Computer Science and Engineering Department at Chalmers University of Technology, where he leads the Safe and Trustworthy Autonomous Reasoning lab (STARlab). Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley, USA, and obtained his Ph.D. in 2019 from Saarland University, Germany. His research focuses on developing theoretical foundations and techniques for constructing safe and reliable autonomous cyber-physical systems. Hazem earned his Ph.D. from Saarland University in 2019, followed by postdoctoral research at UC Berkeley. His educational background has positioned him at the forefront of formal methods research for autonomous systems. His research interests center on the formal specification, verification, and synthesis of cyber-physical systems, with a particular focus on quantitative approaches for verifying and explaining system behavior. He investigates methods to ensure safety and reliability in autonomous systems through runtime monitoring, specification mining, and assurance frameworks. His work bridges theoretical computer science with practical applications in autonomous driving and robotics. An analysis of his recent publications reveals a strong focus on runtime monitoring techniques for operational design domains, specification mining for predictive safety, and frameworks for runtime assurance of cyber-physical systems. His work frequently combines formal methods with machine learning to address safety challenges in autonomous systems, with applications primarily in autonomous driving. Hazem's research is funded by the Wallenberg AI, Autonomous Systems, and Software Program, reflecting the significance and potential impact of his work in the field of assured autonomy. He has supervised several Master's students, including Samuel Collier Ryder and Johannes Holmgren (2025), and Lucas Karlsson and George Kayembe (2024). His teaching includes courses on Formal Methods for Security, Oracle-Guided Inductive Synthesis, and Data Structures and Algorithms. As the leader of STARlab, Hazem directs research on safe and trustworthy autonomous reasoning, focusing on developing methods to ensure the reliability and explainability of autonomous cyber-physical systems through formal verification techniques.

Boyuan ChenView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Robotics
• +6 more

Boyuan Chen is a research scientist at OpenAI and MIT's Department of Electrical Engineering and Computer Science (EECS), specializing in world models, reinforcement learning, and embodied AI. His work bridges AI systems with physical world understanding through innovative approaches to video generation, spatial reasoning, and robotic control. PhD in EECS from MIT with a minor in philosophy Bachelor's degree in computer science and mathematics from UC Berkeley His research focuses on: World Models: Developing architectures like Diffusion Forcing Transformer to enable stable long-horizon video generation and planning Embodied AI: Advancing spatial reasoning capabilities in Vision-Language Models (SpatialVLM) for robotics applications Reinforcement Learning: Creating self-supervised methods for cross-task transfer learning and soft robot control Key article trends: 2025: History-Guided Video Diffusion for flexible temporal conditioning 2024: Diffusion Forcing combining next-token and sequence-level modeling 2024: SpatialVLM introducing 3D spatial reasoning to vision-language models 2023: Self-supervised RL transfer using random features 2021: 3D keypoint learning for robotic control Advising & Collaborations: PhD advisors: Prof. Russ Tedrake and Prof. Vincent Sitzmann (MIT) Undergraduate mentor: Prof. Pieter Abbeel (UC Berkeley) Industry internship: Dr. Fei Xia (DeepMind) Robotics & Team Leadership: Co-founder of MIT's Robomaster ICRA challenge team Founder of high school's first FRC team Developer of personal robots like Autonomous Bogie Rover and Autonomous Drone

Abhishek GuptaView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Robotics
• +7 more

Abhishek Gupta is an Assistant Professor in the Department of Computer Science and Engineering at the Paul G. Allen School of Computer Science & Engineering, University of Washington, where he leads the Washington Embodied Intelligence and Robotics Development (WEIRD) Lab. His research focuses on developing algorithms that enable robotic systems to learn complex tasks in unstructured environments like offices and homes. Dr. Gupta completed his Ph.D. in Machine Learning and Robotics at Berkeley Artificial Intelligence Research (BAIR) at UC Berkeley, advised by Professors Sergey Levine and Pieter Abbeel. Prior to his faculty position, he was a post-doctoral scholar at MIT, collaborating with Russ Tedrake and Pulkit Agarwal. He also earned his bachelor's degree from UC Berkeley. His research interests span computer vision, machine learning, and robotics with particular focus on deep reinforcement learning algorithms that can learn in real-world environments with and around humans. His recent work explores deployment time reinforcement learning, foundation models from off-domain data, fast real-world adaptation, human-in-the-loop interaction, and real-to-sim-to-real policy learning methods. His publications reveal a strong emphasis on practical robotic applications, especially in dexterous manipulation, assistive robotics, and embodied intelligence. Dr. Gupta has received several prestigious recognitions including a Best Paper award at LLARLA workshop at ICML 2018, a RSS 2023 Best Systems Paper Finalist, and multiple spotlight/oral presentations at top conferences including NeurIPS, ICLR, and CoRL. As a principal investigator, Dr. Gupta leads the WEIRD Lab which focuses on developing embodied intelligence for robotics. He actively collaborates with major institutions including MIT, UC Berkeley, and industry partners. His research is funded by grants supporting fundamental advances in robotics and AI, with applications ranging from assistive technologies to industrial automation. Dr. Gupta is currently recruiting highly motivated Ph.D. students and postdoctoral researchers to join his lab.

Luca UlrichView profile

Assistant Professor

• Artificial Intelligence
• Computer Vision
• 3D Graphics
• +5 more

Luca Ulrich is an Assistant Professor at the Politecnico di Torino , Department of Management and Production Engineering (DIGEP). He obtained a Master’s Degree in Computer Engineering (2016) and a PhD in Management, Production, and Design (2021). Research Interests His work focuses on the synergy between Artificial Intelligence (AI) and Extended Reality (XR) for human-centered applications in healthcare, industrial automation, and cultural heritage. Key areas include facial expression recognition, 3D graphics in biometrics, and adaptive human-AI interfaces. Teaching Roles Course Collaborator for 3D Graphics in Biometric Applications (Biomedical Engineering, 2019-2025) Lecturer for Industrial Technical Drawing (Mechanical, Chemical, and Manufacturing Engineering) Scientific Contributions He leads the SMAIL-AI project (2025-2027) and has co-invented three national patents in sports performance analysis, facial recognition, and surgical simulation. His publications span AI in surgery, lip-reading for vocal rehabilitation, and immersive systems. PhD Students Supervised Antonio Lo Faro (Human-AI Interfaces) Rossella Ruggieri (AI in Complex Environments) Chiara Innocente (Behavior Analysis) Federico Salerno (Surgical Guide Analysis) Giorgia Marullo (Phantom Limb Therapy) Francesca Nonis (Facial Emotion Recognition)

Enrico VezzettiView profile

Professor

• 3D Face Analysis
• 3D Scanning
• Computer-Aided Design
• +12 more

Enrico Vezzetti is a Full Professor at the Department of Management and Production Engineering (DIGEP) , Polytechnic University of Turin, Italy. He serves as Coordinator of basic subjects for the 2nd year of Engineering and contributes to doctoral programs in Management and Production Engineering. His research focuses on virtual/digital human behavior analysis for human-centered design , spanning 3D modeling , product lifecycle management (PLM) , and augmented reality (AR) applications in medicine and industry. Research Interests : 3D Face Analysis, Human-Computer Interaction, Product Design, Medical Imaging, Computational Modeling, Industrial Innovation. His recent work explores 3D methodologies for predicting post-surgical facial changes, machine learning in VR environments for stress assessment, and augmented reality systems for orthopedic and maxillofacial surgery. He has developed patented tools for 3D surgical guides and real-time facial expression recognition . He has authored over 100 publications and holds editorial roles in journals like Pattern Recognition and Applied Sciences . His projects include regionally and EU-funded initiatives such as DAS (Design for Augmented Surgery) and Media - Museo Emozionale , focusing on healthcare and cultural preservation. Scientific Awards : Young Researchers - 2011 Award (2011) Polytechnic University of Turin Publications Award (2011, 2012) He leads research groups in 3D Lab and collaborates with institutions on digital twins , smart manufacturing , and medical AI systems , emphasizing cross-fertilization between academia and industry .

Cees SnoekView profile

Researcher

• AI foundation models
• Multimodal learning
• Video classification
• +3 more

Cees Snoek is a researcher at the University of Amsterdam specializing in AI foundation models, multimodal learning, and video analysis. His work focuses on advancing self-supervised learning, generalized category discovery, and multimodal interaction. Research Highlights: Developing revolutionary self-coding models for test-time category discovery Creating methods for generalized multimodal learning with unseen modality combinations Pioneering Bayesian approaches to improve prompt learning in vision-language models Innovating end-to-end graph refinement for object detection Advancing motion-focused video representations through tubelet-contrastive learning His recent work at NeurIPS 2023 and ICCV 2023 demonstrates leadership in solving fundamental challenges in category delineation, multimodal generalization, and 3D point cloud processing. All publications emphasize practical implementation with theoretical foundations. Scientific Awards: Recipient of the Netherlands Prize for ICT research (2012), recognizing innovative contributions to semantic video search technology

Nasim Dadashi SerejView profile

Lecturer

• Artificial Intelligence in Healthcare
• Machine Learning
• Deep Learning
• +7 more

Nasim Dadashi Serej is a Lecturer in Artificial Intelligence at the School of Computing and Engineering, University of West London. Her expertise lies in applying AI to healthcare challenges, particularly in medical data analysis, collaborating closely with clinicians and healthcare organizations. Her research focuses on machine learning, deep learning, computer vision, and medical image-guided interventions. Her research interests include advanced AI techniques such as 3D scene analysis, natural language processing, stochastic search methods, and combinatorial optimization. She actively contributes to the development of software solutions for medical applications like image-guided navigation, medical image/video processing, and dataset collection. Nasim teaches across multiple programs, including BSc and MSc courses in Artificial Intelligence, Data Science, and related fields. Her recent publications span AI-driven healthcare innovations, from seizure detection to cardiovascular imaging analysis and pandemic forecasting. She emphasizes collaboration with clinical partners to ensure practical, real-world impact. Her work bridges theoretical AI advancements with clinical practice, aiming to improve diagnostic accuracy and patient outcomes through technology.

Heng-Yu KuView profile

Professor

• Technology integration
• Online teaching and learning strategies
• Cross-cultural studies
• +3 more

Heng-Yu Ku is a Professor in the School of Teacher Education at the University of Northern Colorado (UNC), part of the College of Education and Behavioral Sciences. He serves as the Program Coordinator for the Technology, Innovation, and Pedagogy Program (since 2019) and has been with UNC since 2011. His research focuses on technology integration, online teaching strategies, and cross-cultural educational studies. He holds a Ph.D. in Learning and Instructional Technology from Arizona State University, alongside an M.T. and B.S. in Industrial Management. Research Highlights: Dr. Ku’s work spans innovative teaching methods, mobile learning readiness in Thailand, and intercultural competence in teacher training. Notable projects include robotics-enhanced computational thinking and cross-cultural education studies in Myanmar and Thailand. Awards: Fulbright Specialist Award (2016, 2023) Arno H. Luker Award for Excellence in Scholarship (2021) College of Education and Behavioral Sciences Outstanding Scholar Award (2005, 2010, 2021) Advising & Grants: As a research advisor, he guided students to the Graduate Dean’s Citation for Excellence Award (2022 and 2024). His work emphasizes collaborative, technology-driven pedagogical advancements.

Richard HanleyView profile

Associate Professor

• Metaphysics
• Philosophy of Language
• Applied Ethics
• +3 more

Richard Hanley is an Associate Professor in the Department of Philosophy at the University of Delaware, affiliated with the College of Arts & Sciences. He holds a Ph.D. from the University of Maryland, College Park and a B.A. from the University of Sydney. His research focuses on metaphysics, philosophy of language, applied ethics, and the philosophy of gender, race, and sexuality. His current work explores the interpretation of law and the metaphysics of time travel. Notable publications include The Metaphysics of Star Trek , The Blackwell Guide to the Philosophy of Language , and South Park and Philosophy . He regularly teaches courses like PHIL 205 (Logic) and PHIL 315 (Metaphysics). His research interests span distributive justice, philosophy of fiction, and the ethical dimensions of science fiction narratives. He has contributed to interdisciplinary discussions on academic freedom, sexual harassment policies, and the ethics of technology through works like Defense against the Dark Arts: Academic Freedom Meets the Antiwoke Crusade .

Safwan WshahView profile

Associate Professor

• Machine Learning
• Deep Learning
• Computer Vision
• +8 more

Safwan Wshah is an Associate Professor in the Department of Computer Science at the University of Vermont (UVM), where he leads the Vermont Artificial Intelligence Laboratory (VaiL). His research focuses on machine learning theory and applications in cross-view geo-localization, geospatial understanding of satellite imagery, and interdisciplinary collaborations in medical, transportation, energy, and communication domains. Ph.D. in Computer Science and Engineering from SUNY Buffalo 19 U.S. patents and 25+ peer-reviewed publications Previously worked at PARC (Palo Alto Research Center), a Xerox company His work bridges deep learning with practical applications in: Image geo-localization from ground/satellite sources Medical imaging and diagnostics Transportation infrastructure analysis Energy system optimization The lab's recent publications demonstrate innovations in diffusion models , generative adversarial networks , and multi-modal datasets like VIGORv2. Current projects include: Machine learning for antimicrobial peptide design (AMPGAN v2) Deep learning frameworks for traffic sign recognition (ARTS dataset) Geo-localization techniques for autonomous systems Key research team members include: Ph.D. candidates: Daniel Wilson, Xiaohan Zhang, Ahmad Arrabi M.S. students: Yuqi Chen, Omar Awajan

Christian BauckhageView profile

Professor

• Machine Learning
• Pattern Recognition
• Quantum Computing
• +2 more

Christian Bauckhage is Professor of Computer Science specializing in Pattern Recognition at the University of Bonn and Lead Scientist for Machine Learning at Fraunhofer IAIS. He serves as co-director of The Lamarr Institute for Machine Learning and Artificial Intelligence. With over 20 years of experience in AI, his current research focuses on quantum computing applications in machine learning. His research spans quantum machine learning, explainable AI, financial document analysis, and medical imaging. Recent publications demonstrate strong focus on quantum computing applications, large language models in finance, and explainability techniques. Bauckhage has published over 200 papers on pattern recognition, machine learning, and quantum computing applications. His research group at Fraunhofer IAIS focuses on industrial applications of AI in finance, healthcare, and agriculture.

Scott NiekumView profile

Associate Professor

• Artificial Intelligence
• Robotics
• Machine Learning
• +4 more

Scott Niekum is an Associate Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst, where he directs the Personal Autonomous Robotics Lab (PeARL). Previously, he held assistant and associate professor positions at the University of Texas at Austin (2015–2022). He earned his PhD in Computer Science from UMass Amherst in 2013 and completed a postdoctoral fellowship at Carnegie Mellon University's Robotics Institute (2013–2015). His research focuses on enabling robots to learn autonomously through interactions with humans and environments, emphasizing safe and efficient algorithms. Key areas include reinforcement learning, imitation learning, human-robot collaboration, and safety guarantees. His work bridges robotics and machine learning to address challenges like reward design, causal reasoning, and generalization from limited data. Notable contributions include the EMPATHIC framework for task learning via implicit human feedback, the PeARL lab's advancements in gaze-guided robotics, and scalable methods for offline reinforcement learning. He has been recognized with the NSF CAREER Award (2023), AFOSR Young Investigator Award, and teaching excellence honors. Dr. Niekum actively serves on conference committees (NeurIPS, ICML, RSS) and journals, promoting rigorous research standards. His lab's innovations aim to deploy autonomous systems in real-world settings like homes and workplaces with minimal expert intervention.

• Reinforcement Learning
• Control Theory
• Information Theory
• +5 more

Roy Fox is an Assistant Professor and founder of the Intelligent Dynamics Lab (indylab) in the Department of Computer Science at the University of California, Irvine. He is affiliated with the Center for Machine Learning and Intelligent Systems (CML) , the Algorithms, Combinatorics and Optimization Center (ACO) , and the Institute for Mathematical Behavioral Sciences (IMBS) . His academic background includes a Ph.D. in Computer Science and Engineering from the Hebrew University of Jerusalem under Naftali Tishby, an MSc from the Technion IIT under Moshe Tennenholtz, and postdoctoral research at UC Berkeley with Ion Stoica (RISELab) and Ken Goldberg (AUTOLAB). Research Interests: Fox focuses on control learning —the intersection of reinforcement learning, control theory, and robotics. His work emphasizes structure, exploration, and optimization in deep control systems for both virtual and physical agents. Key themes include task-relevant perception, safe reinforcement learning, and scalable algorithms for complex decision-making environments. Recent Work Trends: Recent publications highlight advancements in safe exploration (shields for reinforcement learning), state abstraction (selective perception), and language-guided world modeling for embodied agents. His work bridges theoretical foundations with practical applications in robotics and autonomous systems. Labs & Collaborations: The Intelligent Dynamics Lab explores adaptive systems that learn from dynamic environments. Collaborations span machine learning theory, robotics, and interdisciplinary centers like CML and ACO. Fox actively seeks postdoctoral researchers and graduate/undergraduate students to join his team.

• Urology
• Artificial Intelligence in Medicine
• Male Infertility
• +4 more

Jamal Nabhani serves as Assistant Professor of Clinical Urology at the University of Southern California and Assistant Dean for Clinical Administration at Los Angeles General Hospital. His dual roles focus on advancing urological care through academic research and clinical leadership within public health systems serving diverse populations. Dr. Nabhani's research program integrates cutting-edge technology with clinical practice, emphasizing: Artificial intelligence applications for outpatient urology decision support Optimized kidney stone management in public health systems Male infertility mechanisms following testicular trauma Prostate cancer care transitions for low-income populations Robotic and laparoscopic surgical innovations Testicular cancer pathophysiology and metastasis patterns Analysis of his 2021-2025 publications reveals a consistent focus on leveraging AI to improve patient communication and clinical workflows while addressing healthcare disparities in public systems. His work on integrated care models for kidney stone patients and insurance transition challenges for prostate cancer patients demonstrates commitment to vulnerable populations. Surgical technique refinements, particularly in complex tumor thrombectomy and image-guided biopsies, complement his health services research. As Assistant Dean for Clinical Administration at Los Angeles General Hospital, Dr. Nabhani shapes clinical service delivery while maintaining active research productivity. His scholarly contributions appear in leading urology journals including European Urology, Journal of Urology, and Urology, reflecting significant impact in both clinical and translational domains.