Find Academic Supervisors

Anders MiltnerView profile

Assistant Professor

• Program Synthesis
• Bidirectional Programming
• Language Design
• +2 more

Anders Miltner is an Assistant Professor at Simon Fraser University in Canada. He previously worked as a Postdoctoral Fellow at UT Austin and earned his PhD from Princeton University . Education: PhD (Princeton University), Postdoctoral Fellow (UT Austin) Research interests include Program Synthesis , Bidirectional Programming , Language Design , Domain-Specific Languages , and IDE Tools . His work focuses on creating tools and languages that enhance programmer productivity through synthesis techniques and bi-directional transformations. Recent publications include work on DSLs for grammatical domains, recursive functional program synthesis, representation invariants, and edit suggestion synthesis. Articles span conferences like OOPSLA , PLDI , ICFP , and POPL , with an emphasis on formal methods and tool development. He contributes to academic communities as a Committee Member and Co-Chair in tracks like Student Research Competition and Artifact Evaluation Committee , but no specific scientific awards are listed in available data. Advising and mentoring activities include Session Chair roles and Student Research Competition Judge duties. His contributions to GitHub repositories in OCaml , Scala , and TeX demonstrate a practical focus on language implementation and synthesis techniques.

Ravi ChughView profile

Associate Professor

• Programming Languages
• Human-Computer Interaction
• Direct Manipulation Programming Systems
• +3 more

Ravi Chugh is an Associate Professor in the Department of Computer Science at the University of Chicago. His work bridges Programming Languages (PL) and Human-Computer Interaction (HCI), focusing on direct manipulation programming systems and hole-driven development. Affiliation: University of Chicago Role: Director of Undergraduate Studies Research interests: Direct Manipulation Programming Systems Type Systems Program Synthesis Structured Editing Visual Programming His recent work includes Ragged Blocks (UIST 2025) for structured text rendering, Code Style Sheets (OOPSLA 2025) for styling code, and projects like Sketch-n-Sketch and Hazel for bimodal programming environments. Professional Activities: Program Committee: HATRA 2025, VL/HCC 2023, PLDI 2022, POPL 2021 Artifact Evaluation Co-Chair: PLDI 2018-2020 Editorial Board: Journal of Functional Programming (2020–)

Kathleen FisherView profile

Adjunct Professor

• Programming Languages
• Domain-Specific Languages (DSL)
• Program Synthesis
• +4 more

Kathleen Fisher is an Adjunct Professor in the Computer Science Department at Tufts University and currently serves as the Director of the Information Innovation Office at DARPA. She previously held roles as Professor and Department Chair at Tufts (2016-2021), Program Manager at DARPA, and Principal Member of Technical Staff at AT&T Labs Research. Her academic journey began with a PhD in Computer Science from Stanford University. Kathleen’s research focuses on advancing programming languages through domain-specific languages (DSLs), program synthesis, and formal methods. Her work addresses challenges in ad hoc data management, secure systems, and integrating machine learning with programming language design. Notable projects include the Hancock and PADS systems for data processing, Forest for filestore management, and verified parser generators. She has received prestigious accolades, including ACM Fellow, Hertz Foundation Fellow, and SIGPLAN Distinguished Service Award. Her service includes leadership roles in ACM SIGPLAN, CRA-W, and as General Chair for ICFP 2015. Kathleen has advised PhD student Matt Ahrens and led impactful DARPA programs like HACMS and PPAML. As co-founder of the Programming Language Mentoring Workshop (PLMW), she actively contributes to diversity initiatives in computer science. Her research group, TuPL, explores DSLs, program synthesis, and language-based security, maintaining projects such as Autobahn and PADS.

• Graph Theory
• Combinatorics
• Optimization
• +3 more

Laura Gellert is a researcher at the Institute of Optimization and Operations Research at the University of Ulm. Her work focuses on advanced graph theory problems, including colorings, decompositions, and structural properties of complex graphs. Research Interests: Graph coloring, cycle decompositions, quadrangulations, t-perfect graphs, degree sequences, and treewidth-related problems. Awards: No specific awards mentioned. Teaching Activities: She has taught courses such as Graph Theory, Optimization I/II, and seminars on discrete mathematics and optimization at the University of Ulm from 2013 to 2017.

Tillmann RendelView profile

Research Fellow

• Programming Languages
• Software Engineering
• Domain-Specific Languages (DSLs)
• +4 more

Tillmann Rendel is a Research Fellow at the University of Tübingen , affiliated with the Department of Programming Languages and Software Technology . He contributes to projects involving DSLs , language design , and program transformations . Education: M.Sc. in Computer Science. Research interests center on programming languages , focusing on modular systems , functional programming , and domain-specific language implementation . His work explores invertible syntax descriptions , copattern matching , and variability-aware testing . Publications highlight advancements in parsing , DSL design , and language composition , with a strong emphasis on formal semantics , compiler design , and interactive development tools . Scientific Awards: Most Influential Paper Award at GPCE 2018 for 'Polymorphic Embedding of DSLs' (2008 paper co-authored with Hofer, Ostermann, and Moors). Collaborations include key contributions to the Uroboro project , participation in Dagstuhl Seminars , and presentations at major conferences like OOPSLA , ICFP , and DSLDI .

Matias ZurbriggenView profile

Research Professor

• Synthetic Biology
• Optogenetics
• Plant Biology
• +5 more

Matias Zurbriggen is a Research Professor at the Institute of Synthetic Biology, Heinrich Heine University Düsseldorf, where he leads a pioneering research group at the interface of engineering and life sciences. His work focuses on developing theoretical-experimental synthetic biology approaches to understand and control eukaryotic signaling processes with quantitative and spatiotemporal precision. Dr. Zurbriggen's research interests center on synthetic biology principles applied to biological signaling systems, with particular emphasis on optogenetics. He pioneered cellular optogenetics - engineering synthetic photoswitches to control molecular processes with high precision, minimal toxicity, and non-invasive methods. His group has successfully overcome the fundamental challenge of implementing optogenetics in plants, which normally require light for growth, opening new possibilities for plant engineering. His research spans microbial, yeast, mammalian, and plant systems, with applications in tissue engineering, biomedical research, and crop improvement. His recent publications demonstrate significant advances in optogenetic tools, including genetically-stable optogenetic switches for 3D tissue cultures, image-guided optogenetic patterning, and specialized biosensors for plant hormones. These innovations enable precise spatiotemporal control of cellular processes using light of different colors, with applications spanning from fundamental biological research to practical agricultural improvements. Selected by Nature Methods as one of the top methods of 2023 Dr. Zurbriggen's research program integrates synthetic biology with advanced imaging and computational approaches to reconstruct semisynthetic signaling and metabolic networks. His group performs de novo engineering of genetic circuits, biosensors, and synthetic metabolic pathways, including intracellular and cell-cell communication systems. The work has significant implications for understanding developmental processes, metabolic regulation, and creating novel biotechnological applications in medicine and agriculture.

Bruno C. d. S. OliveiraView profile

Associate Professor

• Programming Languages
• Modularity
• Type Systems
• +6 more

Bruno C. d. S. Oliveira is an Associate Professor at the University of Hong Kong , Department of Computer Science. His research focuses on Programming Languages , Modularity , and Type Systems , particularly exploring intersections of Functional and Object-Oriented Programming . He leads the HKU PL Group and actively supervises PhD/MPhil students. Research Interests: Type systems for modularity, subtyping, intersection/union types, gradual typing, polymorphism, and language design. Key Contributions: Unified functional and object-oriented paradigms via object algebras, advanced iso-recursive subtyping theory, and developed practical solutions for the expression problem. Scientific Awards: Distinguished Artifact Award (OOPSLA 2024) Editors' Choice Award (The Art, Science and Engineering of Programming, 2019) Best Paper Awards at ECOOP 2012, Modularity 2016, and TFP 2007 Advising: Supervised 14 PhD/MPhil students between 2007–2024. Students research topics like type inference, visitor frameworks, and disjoint polymorphism.

Armin BahlView profile

Professor

• Systems Neuroscience
• Neural Circuit Computation
• Sensory Integration
• +4 more

Prof. Armin Bahl is a Professor and Emmy Noether Group Leader at the University of Konstanz's Department of Collective Behavior, where he directs the Bahl Lab. His research investigates neural circuit mechanisms underlying sensory integration, decision-making, and collective behavior using larval zebrafish models combined with two-photon imaging, behavioral analysis, and computational modeling. Research focuses on: Behavioral algorithms of sensory-motor transformations Neural basis of evidence accumulation during decision-making Emergent properties in collective behavior Development of advanced microscopy tools for brain imaging Computational modeling of neural circuit dynamics His recent publications (2019-2025) demonstrate expertise in behavioral neuroscience, neural circuit mapping, and interdisciplinary approaches bridging biology with computational techniques. Research consistently features zebrafish models, sensory processing mechanisms, and decision-making paradigms. Awards: Emmy Noether Fellowship He leads an active research group including 8+ PhD students, 4+ postdocs, and multiple undergraduate researchers. The lab collaborates internationally through exchange programs focusing on advanced imaging techniques and computational neuroscience.

Tianyi ZhangView profile

Assistant Professor

• Software Engineering
• Human-Computer Interaction
• Artificial Intelligence
• +5 more

Tianyi Zhang is a Tenure-Track Assistant Professor of Computer Science and Societal Impact Fellow at Purdue University's College of Science, where he leads the Human-Centered Software Systems Lab. His research focuses on building interactive intelligent systems that synergize human expertise with machine intelligence to improve programming productivity and software robustness. Dr. Zhang's research interests span Software Engineering, Human-Computer Interaction, and Artificial Intelligence. His work centers on developing systems that augment human intelligence with data-driven insights and augment machine intelligence with human guidance, primarily for programming domains including software developers, novice programmers, and computer end-users. His research on code mining and visualization helps programmers make more informed decisions through GitHub and Stack Overflow analysis, while his work on program synthesis assists novices with enriched feedback loops and interpretability. His recent publications (2024-2025) demonstrate a strong focus on leveraging large language models for code generation, program repair, and data wrangling, with particular emphasis on interactive systems that incorporate human feedback. This research direction shows consistent growth in understanding the intersection between human cognition and AI capabilities in programming contexts. Awards and Recognition: NSF Career Award for research on safe and reliable LLM-based code generation Amazon Research Award for human-in-the-loop deep learning optimization Best Paper Honorable Mention Award from SIGCHI for visualizing examples of deep neural networks Best Paper Honorable Mention Award from VAHC for interactive cohort analysis Dr. Zhang actively serves the research community as Program Committee member for major conferences including ICSE, ASE, FSE, CHI, and UIST. His service includes chairing workshops and student research competitions, demonstrating his commitment to mentoring the next generation of researchers. His lab develops systems that address real-world challenges in programming productivity and software safety, with applications spanning from autonomous driving systems testing to data science workflows.

NIANYU LIView profile

Researcher

• Self-adaptive Systems
• Cyber-Physical Systems
• Formal Verification
• +4 more

NIANYU LI is an active researcher at ZGC Lab, China specializing in human-involved self-adaptive systems. Their work integrates rigorous modeling techniques to ensure software safety, security, and reliability in dynamic environments, with significant contributions to cyber-physical systems and formal verification methodologies. They earned a Ph.D. in Computer Software and Theory from Peking University in 2021 under Prof. Zhi Jin and Prof. Wenpin Jiao, with additional research experience at the National Institute of Informatics (NII) and Carnegie Mellon University (CMU) under Prof. Zhenjiang Hu and Prof. David Garlan respectively. Research focuses on human-in-the-loop adaptation mechanisms, with expertise spanning requirements modeling, system verification, and safety-critical cyber-physical implementations. Their methodology combines formal control paradigms with practical human factors considerations to address environmental uncertainty in adaptive systems. Publication trends reveal consistent contributions to ASE, ICSE, and specialized conferences like SASMS, with recent work exploring LLM applications in adaptation, federated learning robustness, and SBOM generation. Key themes include verification-driven design, human-system coordination, and empirical validation of adaptive mechanisms in real-world scenarios. As an active community contributor, they serve on program committees for ASE, ACSOS, and ICSE events while maintaining leadership roles in artifact evaluation. Their work bridges theoretical formal methods with practical implementation challenges in adaptive systems. NIANYU LI maintains research continuity through collaborations with Peking University and international institutions, directing focus toward explainable adaptation mechanisms and safety assurance in increasingly complex human-software ecosystems.

• Energy Systems
• Hardware-in-the-Loop Testing
• Renewable Energy Integration
• +2 more

Derk Gonschor is a Researcher at the Department of Engineering and Communication, Bonn-Rhein-Sieg University of Applied Sciences, based in Sankt Augustin, Germany. His office is located in room H 212 at Grantham-Allee 20, with contact telephone +49 2241 865 9956. His research centers on energy systems engineering, specializing in Hardware-in-the-Loop testing methodologies, renewable energy integration for grid stability, battery storage optimization, and bidirectional electric vehicle charging infrastructure. He actively develops solutions for peak load balancing and sustainable energy transitions in both industrialized and developing contexts. Scientific awards: No scientific awards mentioned in available sources. Current research projects: BiSS – Bidirectional Sector coupling & Storage utilization optimization: Led by Prof. Dr. Marco Jung, this project optimizes battery storage and vehicle-to-grid systems to manage volatile energy peak loads, with Derk Gonschor contributing to hardware-in-the-loop validation. GREATER – Growing Rwandan Energy Awareness Through Higher Education: Co-managed with Prof. Dr. Marco Jung, Prof. Dr. Stefanie Meilinger, and Prof. Dr. Tanja Clees, this initiative advances Rwanda's renewable energy adoption through distributed off-grid systems and technical education programs. No dedicated laboratory facilities are specified, though his work involves hardware-in-the-loop test environments for energy system validation within the department's research infrastructure.

• Robotics
• Motion Planning
• Multi-robot Systems
• +3 more

Liding Zhang is a Ph.D. candidate and researcher at the Chair of Robotics, Artificial Intelligence and Real-Time Systems at the Technical University of Munich (TUM), supervised by Prof. Alois Knoll. His work focuses on advanced robotics with emphasis on motion planning and multi-robot systems. His educational background includes: Master’s degree in Mechanical Engineering and Automation Technology from Technical University of Clausthal (2022) Bachelor’s degree in Mechanical Engineering from Rhine-Waal University of Applied Sciences (2020) Zhang's research centers on sampling-based asymptotically optimal motion planning, high-DOF multi-robot manipulation, coordinated control of mobile robot fleets, and real-time performance optimization. His work explores geometric reasoning in complex robotic systems, addressing challenges in manipulation, navigation, and control through innovative algorithmic approaches that balance computational efficiency with solution quality. Key contributions include adaptive sampling techniques and optimization frameworks for dynamic environments. His publication record (2023-2025) demonstrates consistent contributions to top robotics venues including ICRA, IROS, and IEEE Transactions. The body of work reveals a cohesive research trajectory advancing sampling-based planning through innovations in informed search strategies, adaptive batch processing, and physics-inspired optimization. Recurring themes include handling high-dimensional configuration spaces, ensuring real-time performance, and developing robust solutions for multi-robot coordination and deformable object manipulation. Zhang actively contributes to the robotics community as a reviewer for major conferences (ICRA, IROS, Humanoids) and journals (RA-L, T-ASE, T-Mech), and chaired the Robot Motion Planning IV session at IROS 2024. As a researcher, he participates in nationally and EU-funded projects including Bavarian State Project KI.FABRIK. He mentors bachelor's and master's students on topics spanning LLM-based risk mapping, task and motion planning, and multi-robot coordination, with several theses resulting in peer-reviewed publications. He operates within TUM's Robotics, AI and Real-Time Systems group, which pioneers real-time control systems and advanced planning algorithms for industrial and service robotics applications, maintaining strong industry collaborations and state-of-the-art experimental facilities.

• Artificial Intelligence
• Automated Planning
• Symbolic Reasoning
• +6 more

Dr. Peter Kissmann is a researcher in the Computer Science Department at Universität des Saarlandes, Germany, affiliated with the Foundations of Artificial Intelligence (FAI) Group. His work focuses on automated planning systems, particularly in optimal and net-benefit planning domains. Research Interests: His primary research areas include Artificial Intelligence, Automated Planning, Symbolic Reasoning, Heuristic Search, and Knowledge Representation. He specializes in BDD-based planning, variable ordering, and symbolic search algorithms for optimal planning. The 15 most recent publications reflect his sustained contributions to the International Planning Competitions (IPC) from 2008 to 2014, showcasing evolution in planner design, including support for conditional effects, bidirectional search, improved variable ordering, and integration of modern BDD libraries like CUDD. These works demonstrate deep technical innovation in planner efficiency, robustness, and scalability. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no explicit information about students or grant funding in the provided material. However, his leadership in developing and releasing competitive planning systems suggests involvement in research projects, possibly with student contributors or collaboration within the FAI group. Labs and Teams: He is a member of the Foundations of Artificial Intelligence (FAI) Group at Universität des Saarlandes, which develops advanced planning systems and participates in international competitions like the IPC.

Xiao HeView profile

Researcher

• Bidirectional Transformation
• Domain-Specific Languages
• Software Engineering
• +3 more

Xiao He is affiliated with the University of Science and Technology Beijing , actively contributing to academic research in software engineering and programming languages. His work focuses on bidirectional transformations and domain-specific languages.

Jonathan Immanuel BrachthäuserView profile

Researcher

• Programming Languages
• Type Systems
• Compiler Design
• +7 more

Jonathan Immanuel Brachthäuser is a researcher at the University of Tübingen in Germany. He serves as a program committee member across multiple conferences including IWACO, ICFP, POPL, and SPLASH, and has held organizing roles such as Hybridisation Co-Chair and Sponsorship Chair . His research focuses on algebraic effects , effect handlers , and continuation-passing style in programming language design and compilation. Conference Contributions : 15+ peer-reviewed papers on effect systems, type inference, and compilation techniques. Advising & Mentorship : No formal student advising roles listed, but active participation in academic community building. Technical Leadership : Co-chair for workshops and artifact evaluation committees, reflecting his influence in effect-driven language design.